United States Patent Aug. 31, 2021: METHODS AND SYSTEMS OF PRIORITIZING TREATMENTS , VACCINATION , TESTING AND / OR ACTIVITIES WHILE PROTECTING THE PRIVACY OF INDIVIDUALS

US011107588B2
( 12 ) United States Patent
Ehrlich et al .
( 10 ) Patent No .: US 11,107,588 B2
( 45 ) Date of Patent : Aug. 31 , 2021
( 54 ) METHODS AND SYSTEMS OF PRIORITIZING TREATMENTS ,VACCINATION , TESTING AND / OR ACTIVITIES WHILE PROTECTING THE PRIVACY OF INDIVIDUALSHO4W 12/069 ; H04W 4/80 ; GOON 77005 ;
G08B 21/02 ; G07C 9/28 ; G07C 9/22 ;
GOOF 1/163 ; G06F 3/14 ; GO9G 5/36 ;
( Continued )
( 56 ) References Cited
( 71 ) Applicants : Gal Ehrlich , Ramat – Gan ( IL ) ; Maier
Fenster , Petach – Tikva ( IL ) U.S. PATENT DOCUMENTS
( 72 ) Inventors : Gal Ehrlich , Ramat – Gan ( IL ) ; Maier
Fenster , Petach – Tikva ( IL )
7,705,723 B2
8,645,538 B2
4/2010 Kahn et al .
2/2014 Pan
( Continued )
( * ) Notice: Subject to any disclaimer , the term of this OTHER PUBLICATIONS
patent is extended or adjusted under 35
U.S.C. 154 ( b ) by 0 days . Office Action and Search Report dated Dec. 17 , 2020 From the
Israel Patent Office Re . Application No. 276648. ( 9 Pages . ) .
( 21 ) Appl. No .: 17 / 106,279 ( Continued )
( 22 ) Filed : Nov. 30 , 2020 Primary Examiner – Anh V La
( 65 ) Prior Publication Data
US 2021/0082583 A1 Mar. 18 , 2021
( 30 ) Foreign Application Priority DateAug. 11 , 2020
Sep. 1 , 2020
( IL )
276648
276665
277083
( 51 ) Int . CI .
G06Q 10/00 ( 2012.01 )
G16H 50/80 ( 2018.01 )
( Continued )
( 52 ) U.S. Cl .
CPC G16H 50/80 ( 2018.01 ) ; GO6N 77005
( 2013.01 ) ; G16H 50/30 ( 2018.01 ) ; H04W
4/023 ( 2013.01 ) ; H04W 4/029 ( 2018.02 )
( 58 ) Field of Classification Search
CPC G16H 50/80 ; G16H 50/30 ; G16H 15/00 ;
G16H 10/60 ; H04W 4/023 ; H04W 4/029 ;
( 57 ) ABSTRACT

System and methods for anonymously selecting subjects for treatment against an infectious disease caused by a pathogen . The system comprises a plurality of electronic devices comprising instructions to generate an ID and , when in proximity of another such electronic device , one or both
electronic devices transmit / receive the ID to / from the other electronic device . Then , a score is generated based on a plurality of such received IDs . Additionally , based on information received from a server , relevant treatment instructions are displayed to the subjects based on the received information and the score . The server comprises instructions for sending to the plurality of electronic devices the information to be displayed with the relevant treatment instructions , additionally the server and / or the electronic devicescomprise instructions to generate a prediction of likelihood
of a subject transmitting the pathogen , based on the score ofthe subject .
37 Claims , 12 Drawing Sheets
Receiving information about subject 202
Analyzing information 204
Generating score 206
Optionally allocating in score group 208
Vaccinating according to score / group score 210
US 11,107,588 B2
Page 2
( 51 ) Int . Ci .
H04W 4/02 ( 2018.01 )
G16H 50/30 ( 2018.01 )
GOON 700 ( 2006.01 )
H04W 4/029 ( 2018.01 )
( 58 ) Field of Classification Search
CPC GO9G 5/02 ; GO9G 2354/00 ; G06K 7/10366 ;
HO4L 9/30
USPC 340 / 539.13 , 539.12 , 539.23 , 539.11 ;
705/2
See application file for complete search history .
( 56 ) References Cited
U.S. PATENT DOCUMENTS
8,862,448 B2 10/2014 Holmes et al .
9,075,909 B2 7/2015 Almogy et al .
10,275,526 B2 4/2019 Dodge et al .
2004/0236604 Al 11/2004 McNair
2006/0036619 A1 2/2006 Fuerst et al .
2006/0218010 A1 9/2006 Michon et al .
2008/0091471 Al 4/2008 Michon et al .
2009/0319295 A1 12/2009 Kass – Hout et al .
2011/0238432 A1 9/2011 DeLoach
2012/0274464 A1 * 11/2012 Sweeney
2015/0350850 A1 * 12/2015 Edge
2017/0019765 A1 1/2017 Hoyer
2020/0279464 A1 * 9/2020 Llewelyn
2020/0357510 A1 * 11/2020 Bhavani
2020/0388382 A1 * 12/2020 Costantino
2021/0020294 A1 * 1/2021 Bharmi
G06Q 10/087
340 / 539.13
GOIS 5/0018
455 / 456.1
H04W 12/082
G16H 10/60
G16H 40/20
GO6Q 30/0185
G16H 20/30
OTHER PUBLICATIONS
on Intelligent Human Computer Systems for Crisis Response and
Management 2007 , ISCRAM 2007 , p . 295-304 , May 13 , 2007 .
Longini Jr. et al . “ Containing Pandemic Influenza With Antiviral
Agents ” , American Journal of Epidemiology , 159 ( 7 ) : 623-633 , Apr.
1 , 2004 .
Morawska et al . “ Airborne Transmission of SARS – CoV – 2 : The
World Should Face the Reality ” , Environmental International , 139 :
105730-1-105730-4 , Available Online Apr. 10 , 2020 .
Qian et al . “ Ventilation Control for Airborne Transmission of
Human Exhaled Bio – Aerosols in Buildings ” , Journal of Thoracic
Disease , 10 ( Suppl.19 ) : S2295 – S2304 , Jul . 2018 .
Skene et al . “ A Marginal Benefit Approach for Vaccinating Influ
enza ‘ Superspreaders ” ” , Medical Decision Making , 34 ( 4 ) : 536-549 ,
May 2014
Straetemans et al . “ Priorization Strategies for Pandemic Influenza
Vaccine in 27 Countries of the European Union and the Global
Health Security Action Group : A Review ” , BMC Public Health ,
7 ( 1 ) : 236-1-236-12 , Published Online Sep. 7 , 2007 .
Venkatramanan et al . “ Optimizing Spatial Allocation of Seasonal
Influenza Vaccine Under Temporal Constraints ” , PLoS Computa
tional Biology , 15 ( 9 ) : e1007111-1 – e1007111-17 , Published Online
Sep. 16 , 2019 .
Versel “ Smartphone App Seeks ‘ Superspreaders ’ of Flu ” ,
Mobile Health News , 3 P. , Apr. 26 , 2011 .
Who “ Draft Landscape of COVID – 19 Candidate Vaccines ” , 9 P. ,
Aug. 10 , 2020 .
Abbasi et al . “ Modeling Vaccine Allocations in the COVID – 19
Pandemic : A Case Study in Australia ” , Available at SSRN , p . 1-34 ,
Dec. 9 , 2020 .
ACS “ 2011-2015 5 – Year ACS Commuting Flows : Tables ” , ACS , 1
P. , Last Revised Oct. 24 , 2019 .
Ahmed et al . “ A Survey of COVID – 19 Contact Tracing Apps ” ,
IEEEAccess , 8 : 134577-134601 , Published Online Jul . 20 , 2020 .
Anderson et al . “ Challenges in Creating Herd Immunity to SARS
CoV – 2 Infection by Mass Vaccination ” , The Lancet , 396 ( 10263 ) :
1616-1618 , Published Online Nov. 4 , 2020 .
Anglemyer et al . “ Digital Contact Tracing Technologies in Epidem
ics : A Rapid Review ” , Cochrane Database of Systematic Reviews ,
2020 ( 8 / Art.No . CD013699 ) : 1-45 , Aug. 18 , 2020 .
Aspnes et al . “ Inoculation Strategies for Victims of Viruses and the
Sum – of – Squares Partition Problem ” , Journal of Computer and Sys
tem Sciences , 72 ( 6 ) : 1077-1093 , Available Online Apr. 17 , 2006 .
Barabasi et al . ” Emergence of Scaling in Random Networks ” ,
Science , 286 ( 5439 ) : 509-513 , Oct. 15 , 1999 .
Barrett et al . “ Generation and Analysis of Large Synthetic Social
Contact Networks ” , Proceedings of the 2009 Winter Simulation
Conference , WSC , Austin TX , USA , Dec. 13-16 , 2009 , p . 1-12 ,
Dec. 13 , 2009 .
Beckman et al . “ Creating Synthetic Baseline Populations ” , Trans
portation Research Part A : Policy and Practice , 30 ( 6 ) : 415-429 ,
Nov. 1996 .
Bertsimas et al . “ Optimizing Vaccine Allocation to Combat the
COVID – 19 Pandemic ” , MedRxiv Preprint , p . 1-27 , Posted Nov. 18 ,
2020 .
Bollobas et al . “ Robustness and Vulnerability of Scale – Free Ran
dom Graphs ” , Internet Mathematics , 1 ( 1 ) : 1-35 , Jan. 2004 .
Bubar et al . “ Model – Informed COVID – 19 Vaccine Prioritization
Strategies by Age and Serostatus ” , Science , 371 ( 6532 ) : 916-921 ,
Feb. 26 , 2021 .
Buckner et al . “ Optimal Dynamic Prioritization of Scarce COVID
19 Vaccines ” , MedRxiv Preprint , p . 1-37 , Sep. 22 , 2020 .
Cattuto et al . “ Dynamics of Person – to – Person Interactions From
Distributed RFID Sensor Networks ” , PLoS One , 5 ( & ) : e11596-1
e11596-9 , Jul . 15 , 2010 .
CDC “ COVID – 19 Pandemic Planning Scenarios ” , Centers for Dis
ease Control and Prevention , CDC , Office of the Assistant Secretary
for Preparedness and Response , ASPR , p . 1-9 , Updated Sep. 10 ,
2020 .
CDC “ COVID – 19 Pandemic Planning Scenarios ” , Centers for Dis
ease Control and Prevention , CDC , p . 1-9 , Updated Mar. 19 , 2021 .
Centre for Time Use Research “ Multinational Time Use Study ” ,
Centre for Time Use Research , p . 1-3 , 2021 .
Office Action and Search Report dated Dec. 17 , 2020 From the
Israel Patent Office Re . Application No. 276665. ( 9 Pages ) .
Office Action and Search Report dated Dec. 17 , 2020 From the
Israel Patent Office Re . Application No. 277083. ( 10 Pages ) .
Translation Dated Jan. 3 , 2021 of Office Action dated Dec. 17 , 2020
From the Israel Patent Office Re . Application No. 276665. ( 5
Pages ) .
Translation Dated Jan. 3 , 2021 of Office Action dated Dec. 17 , 2020
From the Israel Patent Office Re . Application No. 277083. ( 5
Pages ) .
Translation Dated Jan. 3 , 2021 of Office Action Report dated Dec.
17 , 2020 From the Israel Patent Office Re . Application No. 276648 .
( 5 Pages ) .
Ahmed et al . “ Coronavirus Disease 2019 ( COVID – 19 ) Complicated
by Acute Respiratory Distress Syndrome : An Internist’s Perspec
tive ” , Cureus , 12 ( 3 ) : e7482-1 – e7482-7 , Published Online Mar. 31 ,
2020 .
Ben Tovim “ The Ministry of Health Stops the Development of the
” Shields ’ : It Prefers to Force You to Install A New Application ” ,
Geek Time , p . 1-9 , Dec. 1 , 2020 .
Britton et al . “ A Mathematical Model Reveals the Influence of
Population Heterogeneity on Herd Immunity to SARS – CoV – 2 ” ,
Science , 369 ( 6505 ) : 846-849 , Aug. 14 , 2020 .
Chen et al . “ Next Generation Technology for Epidemic Prevention
and Control : Data – Driven Contact Tracking ” , IEEE Access , 7 :
2633-2642 , Dec. 24 , 2018 .
Cox “ The Vulnerable Can Wait . Vaccinate the Super – Spreaders
First . Who Gets Priority When Covid – 19 Shots Are in Short Supply ?
Network Theorists Have A Counterintuitive Answer : Start With the
Sociel Butterflies ” , Wired , Avid Reader Press , p . 1-20 , Dec. 2020
Jan. 2021 .
IBM Research Editorial Staff “ Tracking Tuberculosis in South
Africa ” , IBM Research Blog , 7 P. , Oct. 11 , 2016 .
Jenvald et al . “ Simulation as Decision Support in Pandemic Influ
enza Preparedness and Response ” , Proceedings of the Conference
US 11,107,588 B2
Page 3
( 56 ) References Cited
OTHER PUBLICATIONS
Chan et al . “ PACT : Privacy – Sensitive Protocols and Mechanisms
for Mobile Contact Tracing ” , ArXiv Preprint ArXiv : 2004.03544v4 ,
p . 1-22 , May 7 , 2020 .
Chen et al . “ Medical Costs of Keeping the US Economy Open
During COVID – 19 ” , Scientific Reports , 10 ( 1 ) : 18422-1-18422-10 ,
Oct. 28 , 2020 .
Cohen et al . “ Efficient Immunization Strategies for Computer
Networks and Populations ” , ArXiv Preprint ArXiv : Cond – Mat /
0207387v3 , p . 1-5 , Dec. 10 , 2003 .
Deming et al . “ On A Least Squares Adjustment of A Sampled
Frequency Table When the Expected Marginal Totals Are Known ” ,
The Annals of Mathematical Statistics , 11 ( 4 ) : 427-444 , Dec. 1940 .
Draief et al . “ Thresholds for Virus Spread on Networks ” , The
Annals of Applied Probability , 18 ( 2 ) : 359-378 , Apr. 2008 .
Eames et al . “ Epidemic Prediction and Control in Weighted Net
works ” , Epidemics , 1 ( 1 ) : 70-76 , Published Online Dec. 25 , 2008 .
Eubank et al . “ Modelling Disease Outbreaks in Realistic Urban
Social Networks ” , Nature , 429 ( 6988 ) : 180-184 , May 13 , 2004 .
Eubank et al . “ Structure of Social Contact Networks and Their
Impact on Epidemics ” , Discrete Methods in Epidemiology , DIMACS
Series Diescrete Mathematics and Theoretical Computer Science ,
American Mathematic Society , 70 : 181-1-181-32 , 2021 .
Ferretti et al . “ Quantifying SARS – CoV – 2 Transmission Suggests
Epidemic Control With Digital Contact Tracing ” , Science , 368 ( 6491 ) :
eabb6936-1 – eabb6936-7 , Published Online Mar. 31 , 2020 .
FHWA “ National Household Travel Survey ” , U.S. Department of
Transportation , Federal Highway Administration , FHWA , 2 P. , Mar.
29 , 2021 .
Foy et al . “ Comparing COVID – 19 Vaccine Allocation Strategies in
India : A Mathematical Modelling Study ” , International Journal of
Infectious Diseases , 103 : 431-438 , Published Online Dec. 31 , 2020 .
Ganesh et al . “ The Effect of Network Topology on the Spread of
Epidemics ” , Proceedings IEEE 24th Annual Joint Conference of the
IEEE Computer and Communications Societies , Miami , FL , USA ,
Mar. 2005 , p . 1-12 , Mar. 13 , 2005 .
Gayle et al . “ Framework for Equitable Allocation of COVID – 19
Vaccine ” , Committee of Equitable Allocation of Vaccine for the
Novel Coronavirus , Board on Health Sciences Policy , Board on
Population Health and Public Health Practice , Health and Medicine
Division , A Consensus Study Report of The National Academies of
Sciences Engineering Medicine , National Academy of Medi
cine , p . i – xx , 1-252 , 2020 .
Genois et al . “ Can Co – Location Be Used as A Proxy for Face – to
Face Contacts ? ” , EPJ Data Science , 7 ( 1 ) : 11-1-11-18 , Dec. 2018 .
Germann et al . “ Mitigation Strategies for Pandemic Influenza in the
United States ” , Proc . Natl . Acad . Sci . USA , PNAS , 103 ( 15 ) : 5935
5940 , Apr. 11 , 2006 .
Gillespie “ A General Method for Numerically Simulating the Sto
chastic Time Evolution of Coupled Chemical Reactions ” , Journal of
Computational Physics , 22 ( 4 ) : 403-434 , Dec. 1976 .
GitHub Microsoft “ Computer Generated Building Footprints for the
United States ” , GitHub – Microsoft / USBuilding Footprints , Version
v2.0 , p . 1-9 , Jul . 13 , 2018 .
GitHub Nytimes “ An Ongoing Repository of Data on Coronavirus
Cases and Deaths in the U.S. ” , GutHub Nytimes / Covid – 19 Data ,
The New York Times , p . 1-12 , 2021 .
Halloran et al . “ Modeling Targeted Layered Containment of An
Influenza Pandemic in the United States ” , Proc . Natl . Acad . Sci .
USA , PNAS , 105 ( 12 ) : 4639-4644 , Mar. 25 , 2008 .
Hayrapetyan et al . “ Unbalanced Graph Cuts ” , ESA’05 , Proceedings
of the 13th Annual European Conference on Algorithms, LNCS ,
3669 : 191-202 , Oct. 3 , 2005 .
Hogan et al . “ Report 33 : Modelling the Allocation and Impact of A
COVID – 19 Vaccine ” , Imperial College London , p . 1-21 , Sep. 25 ,
2020 .
Kretschmar et al . “ Impact of Delays on Effectiveness of Contact
Tracing Strategies for COVID – 19 : A Modelling Study ” , Lancet
Public Health , 5 ( 8 ) : e452-459 , Published Online Jul . 16 , 2020 .
Kumar et al . “ Existence Theorems and Approximation Algorithms
for Generalized Network Security Games ” , 2010 IEEE 30th Inter
national Conference on Distributed Computing Systems , Genoa ,
Italy , Jun . 21-25 , 2010 , p . 1-19 , Jun . 21 , 2010 .
Lipsitch et al . “ Understanding COVID – 19 Vaccine Efficacy : Vac
cine Efficacy in High – Risk Groups and reduced Viral Shedding Are
Important for Protection ” , Science , 370 ( 6518 ) : 763-766 , Nov. 13 ,
2020 .
Lofgren et al . “ Opinion : Mathematical Models : A Key Tool for
Outbreak Response ” , Proc . Natl . Acad . Sci . USA , PNAS , 111 ( 51 ) :
18095-18096 , Dec. 23 , 2014. & Correction , 112 ( 2 ) : E234 , Jan. 13 ,
2015 .
Lum et al . “ A Two – Stage , Fitted Values Approach to Activity
Matching ” , International Journal of Transportation , 4 ( 1 ) : 41-56 ,
2016 .
Machi et al . “ Scalable Epidemiological Workflows to Support
COVID – 19 Planning and Response ” , MedRxiv Preprint , p . 1-19 ,
Posted Online Feb. 26 , 2021 .
Marathe et al . “ Computational Epidemiology ” , KDD ’14 , Proceed
ings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining , New York City , NY , USA ,
Aug. 24-27 , 2014 , Slide Show , p . 1-209 , Aug. 24 , 2014. ( Part I ) .
Marathe et al . “ Computational Epidemiology ” , KDD ’14 , Proceed
ings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining , New York City , NY , USA ,
Aug. 24-27 , 2014 , Slide Show , p . 1-209 , Aug. 24 , 2014. ( Part II ) .
Marathe et al . “ Computational Epidemiology ” , KDD ’14 , Proceed
ings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining , New York City , NY , USA ,
Aug. 24-27 , 2014 , Slide Show , p . 1-209 , Aug. 24 , 2014. ( Part III ) .
Matrajt et al . ” Vaccine Optimization for COVID – 19 : Who to Vac
cinate First ? ” , MedRxiv Preprint , p . 1-84 , Posted Nov. 10 , 2020 .
May et al . “ Spatial Heterogeneity and the Design of Immunization
Programs ” , Mathematical Biosciences , 72 ( 1 ) : 83-111 , Nov. 1984 .
Medlock ” Optimizing Influenza Vaccine Distribution ” , Clemson
University , Department of Mathematical Sciences , p . 1-29 , Aug. 3 ,
2009 .
Pastor – Satorras et al . “ Epidemic Processes in Complex Networks ” ,
ARXiv Preprint ArXiv : 1408.2701v2 , p . 1-62 , Sep. 18 , 2015 .
Pastor – Satorras et al . “ Immunization of Complex Networks ” , ArXiv
Preprint ArXiv : Cond – Math / 010766v2 , p . 1-9 , Apr. 11 , 2002 .
Preciado et al . “ Optimal Resource Allocation for Networks Protec
tion Against Spreading Processes ” , ArXiv Preprint ArXiv : 1309 .
6270v2 , p . 1-10 , May 11 , 2014 .
Preciado et al . “ Optimal Vaccine Allocation to Control Epidemic
Outbreaks in Arbitrary Networks ” , ArXiv Preprint ArXiv : 1303 .
3984v1 , p . 1-8 , Mar. 16 , 2013 .
Saad – Roy et al . “ Immune Life History , Vaccination , and the Dynam
ics of SARS – CoV – 2 Over the Next 5 Years ” , Science , 370 ( 6518 ) :
811-818 , Nov. 13 , 2020 .
Saha et al . “ Approximation Algorithms for Reducing the Spectral
Radius to Control Epidemic Spread ” , ArXiv Preprint ArXiv : 1501 .
066lv1 , p . 1-14 , Jan. 26 , 2015 .
Sambaturu et al . “ Designing Effective and Practical Interventions to
Contain Epidemics ” , Proceedings of the 19th International Confer
ence on Autonomous Agents and MultiAgent Systems , AAMAS
2020 , Auckland , New Zealand , May 9-13 , 2020 , p . 1187-1195 , May
9 , 2020 .
Tennenholtz et al . “ Sequential Vaccination for Containing Epidem
ics ” , MedRxiv Preprint , p . 1-16 , Posted Apr. 14 , 2020 .
Tong et al . “ Gelling , and Melting , Large Graphs by Edge Manipu
lation ” , Proceedings of the 21st ACM International Conference on
Information and Knowledge Management , CIKM’12 , Maui , HI ,
USA , Oct. 29 – Nov . 2 , 2012 , p . 245-254 , Oct. 29 , 2012 .
U.S Bureau of Labor Statistics “ American Time Use Survey ( ATUS ) ” ,
U.S Bureau of Labor Statstics , p . 1-4 , Jun . 25 , 2020 .
U.S. Census Bureau “ American Community Survey 2013-2017
5 – Year Data Release : Median Household Income , Poverty Rates and
Computer and Internet Use ” , U.S Census Bureau , Press Release , 7
P. , Dec. 6 , 2018 .
Van Miegham et al . ” Virus Spread in Networks ” , IEEE / ACM
Transactions on Networking , 17 ( 1 ) : 1-14 , Published Online Jun . 24 ,
2008 .
US 11,107,588 B2
Page 4
( 56 ) References Cited
OTHER PUBLICATIONS
Van Mieghem et al . “ Decreasing the Spectral Radius of a Graph by
Link Removals ” , Physical Review E , 84 ( 1 ) : 016101-1-016101-12 ,
Jul . 6 , 2011 .
Venkatramanan et al . “ Optimizing Spatial Allocation of Seasonal
Influenxa Vaccine Under Temporal Constraints ” , PLoS One , Com
putational Biology , 15 ( 9 ) : e1007111-1 – e1007111-17 , Sep. 16 , 2019 .
Venkatramanan et al . “ Spatio – Temporal Optimization of Seasonal
Vaccination Using A Metapopulation Model of Influenza ” , 2017
IEEE INternational Conference on Healthcare Informatics , ICHI ,
Park City , UT , USA , Aug. 23-26 , 2017 , p . 134-144 , Aug. 23 , 2017 .
Wilder et al . “ Preventing Infectious Disease in Dynamic Popula
tions Under Uncertainty ” , The Thirty – Second AAAI Conference on
Artificial Intelligence , Computational Sustainability and Artificial
Intelligence , AAAI – 18 , 32 ( 1 ) : 841-848 , Apr. 25 , 2018 .
Yang et al . “ Efficient Vaccination Strategies for Epidemic Control
Using Network Information ” , Epidemics , 27 : 115-122 , Published
Online Mar. 6 , 2019 .
Zhang et al . “ Near – Optimal Algorithms for Controlling Propagation
at Group Scale on Networks ” , IEEE Transactions on Knowledge
and Data Engineering , 28 ( 12 ) : 3339-3352 , Published Online Sep. 1 ,
2016 .
* cited by examiner
U.S. Patent Aug. 31 , 2021 Sheet 1 of 12 US 11,107,588 B2
G Superspreader no
Non – Superspreader
Figure 1
U.S. Patent Aug. 31 , 2021 Sheet 2 of 12 US 11,107,588 B2
Receiving information about subject 202
Analyzing information 204
Generating score 206
Optionally allocating in score group 208
Vaccinating according to score / group score 210
Figure 2
U.S. Patent Aug. 31 , 2021 Sheet 3 of 12 US 11,107,588 B2
Receiving information
about subject 302
Evaluating source of
information
304
306 308 AR
Electronic
information
Geographical
information
S.
Governmental
information
Human
information
310 312 SR ( … ) AS
X
RO
Generating a weighted score to
each information according to a
predetermined criteria
314
Generating a total score from the
different weighted scores according 316
to a predetermined criteria
Vaccinating according to the total 318
score
Figure 3
U.S. Patent Aug. 31 , 2021 Sheet 4 of 12 US 11,107,588 B2
NA
TE
ww.w os
?
Sasapain
402 408
******* 3
?
.
Les ***
Figure 4
U.S. Patent Aug. 31 , 2021 Sheet 5 of 12 US 11,107,588 B2
Individual downloads application into
electronic device 502
Optionally individual provides an ID
? B C ?
w E
Receiving scale of scores from server 512
Comparing score with scale of scores 514
Providing user notification related to
treatment
516
Figure 5a
U.S. Patent Aug. 31 , 2021 Sheet 6 of 12 US 11,107,588 B2
?
When electronic devices are in proximity –
Exchanging individual’s full ID between
electronic devices
506
After a period time , analyze IDs stored in
electronic device and generate score 508
YES
510
Completely
anonymous
methods
NO
(E F
Figure Sb
U.S. Patent Aug. 31 , 2021 Sheet 7 of 12 US 11,107,588 B2
()0B
When electronic devices are in proximity –
Exchanging individual’s partial ID between
electronic devices
524
After a period time , analyze IDs stored in
electronic device and generate score 526
528
Completely
anonymous
methods
NO
E F
Figure 5c
U.S. Patent Aug. 31 , 2021 Sheet 8 of 12 US 11,107,588 B2
C
When electronic devices are in proximity –
Exchanging individual’s partial IDs between
electronic devices
530
532 After a period time , analyze partial IDs stored
in electronic device
1 XXXXXXX
After that period time , change the individual’s
partial ID with a new one 534
YES
536
Completely
anonymous
methods
NO
E F
Figure 5d
U.S. Patent Aug. 31 , 2021 Sheet 9 of 12 US 11,107,588 B2
()0D
When electronic devices are in proximity –
Exchanging individual’s partial IDs between
electronic devices 100 % of the times ; and X %
of the times exchange a second ID number ;
where X lower than 100
538
After a period time , analyse partial IDs and
second ID numbers stored in electronic device 540
Optionally , after that period time , change the
individual’s partial ID with a new one 542
544
YES NO Completely
anonymous
methods
F
Figure 5e
U.S. Patent Aug. 31 , 2021 Sheet 10 of 12 US 11,107,588 B2
best
Sending score to server 518
Comparing scores and generating treatment
list 520
Sending back to electronic device notification
related to vaccination 522
Figure 5f
U.S. Patent Aug. 31 , 2021 Sheet 11 of 12 US 11,107,588 B2
Device A meets Device B 602
Device B provides ID and information about
previous meetings 604
Device A evaluates data 606
How many meetings had Device B 608
What kind of individuals had
Device B met 610
Generating a score to meeting with Device B
according to data evaluation 612
Save score in Device A 614
Figure 6
U.S. Patent Aug. 31 , 2021 Sheet 12 of 12 US 11,107,588 B2
Analytics module
708
3rd Party 706
Database
databases and module
sources 702
Server / Computer
User 712
device 710
Simulation
module
Prediction
module
Vaccination and / or prophylactic
treatments order list
Messages to
individuals
718
Figure 7
US 11,107,588 B2
1 2
METHODS AND SYSTEMS OF often present with shortness of breath and pulmonary infil
PRIORITIZING TREATMENTS , trates , the diagnosis of PE may be overlooked in the context
VACCINATION , TESTING AND / OR of an ARDS diagnosis .
ACTIVITIES WHILE PROTECTING THE A research article by Straetemans et . al . called “ Prioriti
PRIVACY OF INDIVIDUALS 5 zation strategies for pandemic influenza vaccine in 27
countries of the European Union and the Global Health
RELATED APPLICATIONS Security Action Group : a review ” discussed vaccine priori
tization strategies during pandemic times , but its conclu
This application claims the benefit of priority of Israel sions are limited to the critical groups , for example , health
Patent Application No. 277083 filed on Sep. 1 , 2020 , Israel 10 care providers ( e.g. , doctors , nurses , laboratories , hospitals ,
Patent Application No. 276665 filed on Aug. 11 , 2020 , and etc. ) , essential service providers ( e.g. , police , fire fighters ,
Israel Patent Application No. 276648 filed on Aug. 11 , 2020 . public sector personnel , governmental personnel , etc. ) and
The contents of the above applications are all incorporated high risk individuals ( e.g. , people with high risk of compli
by reference as if fully set forth herein in their entirety . cations , pregnant women , children , etc. ) . These obvious
This application is also related to United Arab Emirates 15 groups usually amount to less than 2-10 % of the total
Patent Application No. P6001304 / 2020 filed on Sep. 17 , population , which still leaves the government with the
2020 , the contents of which are incorporated herein by question of what is the best order to vaccinate the rest of the
reference in their entirety . population , namely prioritizing vaccinations .
20
C.
FIELD AND BACKGROUND OF THE SUMMARY OF THE INVENTION

INVENTION: Following is a non – exclusive list including some
The present invention , in some embodiments thereof , examples of embodiments of the invention . The invention
relates to methods and systems of prioritizing also includes embodiments , which include fewer than all the
vaccinations treatments \ testing and , more particularly , but 25 features in an example , and embodiments using features
not exclusively , to method and systems of prioritizing from multiple examples , also if not expressly listed below .
vaccinations treatments \ testing in a pandemic situation , Example 1. An anonymized method of treating subjects
whereby vaccines are at short supply and while protecting against an infectious disease caused by a pathogen , com
the privacy of the individuals in the population . prising :
Coronavirus disease 2019 ( COVID – 19 ) is an infectious 30 a . providing an electronic device with proximity tracking
disease caused by severe acute respiratory syndrome coro- circuitry for each of said subjects ;
navirus 2 ( SARS – CoV – 2 ) . It was first identified in December b . generating an ID for each said electronic device ;
2019 in Wuhan , Hubei , China , and has resulted in an a proximity event , when a particular said electronic
ongoing pandemic . The first confirmed case has been traced device of a particular said subject is in proximity of one or
back to 17 Nov. 2019 in Hubei . As of 6 August 2020 , more 35 more other of said electronic devices , one or both of
than 18.7 million cases have been reported across 188 transmitting said ID or an indication thereof to said one or
countries and territories , resulting in more than 706,000 more other devices and receiving an ID or indication thereof
deaths . More than 11.3 million people have recovered . The from said one or more other devices , by said particular
virus is primarily spread between people during close con- electronic device ;
tact , most often via small droplets produced by coughing , 40 d . generating , by said particular electronic device a score
sneezing , and talking . The droplets usually fall to the ground reflecting a propensity for proximity , according to a plurality
or onto surfaces rather than travelling through air over long of received IDs ;
distances . However , the transmission may also occur e . generating for said particular electronic device a pri
through smaller droplets that are able to stay suspended in oritization of treatment based on said score ;
the air for longer periods of time in enclosed spaces , as 45 f . treating said particular subject according to said priori
typical for airborne diseases . Less commonly , people may tization .
become infected by touching a contaminated surface and Example 2. The method according to example 1 , wherein
then touching their face . It is most contagious during the first said generating an ID comprises generating an ID having
three days after the onset of symptoms , although spread is fewer than 100,000 potential values .
possible before symptoms appear , after they disappear and 50 Example 3. The method according to example 2 , wherein
from people who show very mild or do not show symptoms said generating an ID comprises generating a unique ID and
at all . also generating said ID as a portion of said unique ID .
In addition , about 5 % of COVID – 19 patients experience Example 4. The method according to example 1 , further
complications including septic shock , acute respiratory dis- comprising changing said ID periodically .
tress syndrome ( ARDS ) , acute cardiac or kidney injury , and 55 Example 5. The method according to example 1 , further
disseminated intravascular coagulation ( DIC ) . These com- comprising generating a second ID and transmitting said
plications are thought to be manifestations of the cytokine second ID or indication thereof together with said ID .
storm triggered by the host immune response of the virus . In Example 6. The method according to example 5 , wherein
critically ill patients , ARDS was the most common compli- said transmitting a second ID is carried out only at a fraction
cation in 67 % of the patients with a 28 – day mortality of 60 of said proximity events .
61.5 % . DIC has been widely reported in COVID – 19 . Pul- Example 7. The method according to example 6 , wherein
monary embolism ( PE ) in COVID – 19 patients has been said transmitting comprises transmitting also second IDs
reported in a few studies . A recent study pointed to a higher previously received from others of said electronic devices .
incidence of PE with 23 % in severe COVID – 19 patients . The Example 8. The method according to example 6 , com
relationship between virally triggered inflammation , venous 65 prising generating an indication of closeness of a population
thromboembolism , and ARDS in COVID – 19 is still under met by said electronic device based on said received second
investigation . Given that patients with severe COVID – 19 IDs .
US 11,107,588 B2
3 4
Example 9. The method according to example 1 , wherein Example 22. The system according to example 19 ,
said score depends on an estimation of propensity of prox- wherein said server is configured with instructions to receive
imity of said one or more other devices . anonymous scores for a plurality of said electronic devices
Example 10. The method according to example 1 , and use said received scores to generate said general infor
wherein said generating said score comprises counting the 5 mation , said electronic devices configured to use said gen
number of received IDs . eral information to determine a relative treatment priority for
Example 11. The method according to example 10 , their respective subjects .
wherein said counting comprises counting unique IDs . Example 23. The system according to example 19 ,
Example 12. The method according to example 10 , wherein said electronic devices comprises a proximity
wherein said counting comprises counting IDs with a 10 detecting module using one or more of :
weighted parameter , said weighted parameter is generated a . physical proximity data received by means of electronic
by analyzing said exchanged second IDs . positioning data of said subject ;
Example 13. The method according to example 1 , b . a distance indicating sensor which indicates physical
wherein said generating for said particular device comprises proximity of the location of a device in relation to the
transmitting said score to a server and generating said 15 location of said another device ; and
prioritization on said server . c . historical location data .
Example 14. The method according to example 13 , Example 24. The system according to example 19 ,
wherein generating said prioritization comprises comparing wherein said at least one server or said electronic devices
scores by different ones of said electronic devices . comprise instructions to determine a treatment prioritization
Example 15. The method according to example 1 , 20 based on said likelihood .
wherein said generating for said particular device comprises Example 25. The system according to example 23 ,
generating said prioritization on said particular electronic wherein said determine a treatment prioritization further
device . comprises one or more of :
Example 16. The method according to example 15 , a . generating a score component based on a nature of a
wherein said generation comprises receiving form a server a 25 location where said physical proximity data is related ;
list or a function indication prioritization according to score . b . generating a score component comprising health data of
Example 17. The method according to example 1 , com- the subject of one or both electronic devices ;
prising displaying treatment instructions on said particular c . generating a score component comprising a profession of
electronic device based on said generated prioritization . the subject of one or both electronic devices ;
Example 18. The method of example 1 , wherein said 30 d . generating a score component reflecting relative health
pathogen comprises a corona virus and wherein said treat- risk to said subject if said subject contracts said pathogen ;
ment comprises a vaccination and wherein said prioritization and
is used select subjects at greater risk of transmitting the e . generating a score component reflecting damage to soci
pathogen during a pandemic to be vaccinated sooner than ety if said subject contracts said pathogen .
subjects less likely to transmit the pathogen . Example 26. The system according to example 23 ,
Example 19. A system for anonymously selecting subjects wherein when said physical proximity data is related to a
for treatment against an infectious disease caused by a location that is either indoors or in a closed space , then said
pathogen , comprising : predicted likelihood of said subject of transmitting said
a . a plurality of electronic devices configured to be carried pathogen increases by a factor of between about 10 times to
around by said subjects and configured with instructions to : 40 about 100 times .
i . generate an ID comprising for each said electronic Example 27. The system according to example 19 , further
device ; comprising a vaccination server , which allocates vaccina
ii . when in proximity of another such electronic device , tions for a corona virus according to , said displayed treat
one or both of transmit said ID or an indication thereof ment information .
to said another electronic device and receive an ID or 45 Example 28. The system according to example 27 ,
indication thereof from said another electronic device ; wherein said server comprises a simulation module config
iii . generating , a score based on a plurality of such ured to perform one or both of :
received IDs ; ( a ) predict the effect of vaccination on disease spread ;
iv . receiving information from a server ; ( b ) predict the effect of an ID transmission probability on
V. displaying relevant treatment instructions to said sub- 50 distinguishing between subjects who contact mainly sub
jects based on said received information ; jects in a same subpopulation .
b . at least one server comprising a memory and a plurality Example 29. The system of example 19 , wherein said
of modules ; said memory – comprising instructions for : electronic devices are configured to transmit a second ID
vi . sending to said plurality of electronic devices infor- and previously received second IDs , at a probability of less
mation usable by a circuitry in said plurality of elec- 55 than 10 % and using said received second IDs to generate
tronic devices to display said relevant treatment said score .
instructions , Example 30. The system of example 19 , wherein said
wherein said at least one server or said electronic devices transmitted ID is a non – unique ID having fewer possible
comprise instructions to generate a prediction of likelihood values than 10 % of the number of said devices .
of a subject transmitting said pathogen , based on a score of 60 According to an aspect of some embodiments of the
the subject . present invention there is provided a method of selecting
Example 20. The system according to example 19 , subjects for being vaccinated / treated against an infectious
wherein said information comprises one or more of subject disease caused by a pathogen , using personal physical
specific information . proximity information of a subject , comprising :
Example 21. The system according to example 19 , 65 a . generating , by circuitry , a predicted likelihood of said
wherein said information comprises general information subject of transmitting said pathogen based on said physical
usable by a plurality of subjects and devices thereof . proximity information , for a plurality of subjects ;
35
15
US 11,107,588 B2
5 6
b . selecting subjects of said plurality of subjects for According to some embodiments of the invention , the
vaccinating / treating based on a prediction that said vacci- method further comprising vaccinating / treating said subjects
nating / treating said subjects will reduce a likelihood of according to said score .
spreading of said disease in said plurality of subjects , According to some embodiments of the invention , said
wherein said selecting is based on said generated predicted 5 generating a score further comprises a third score compo
likelihood . According to some embodiments of the invention , said nent reflecting relative health risk to said subject if said subject contracts said pathogen . pathogen is selected from the group consisting of a virus, a According to some embodiments of the invention , said bacterium , a fungus and a protozoan .
According to some embodiments of the invention , said 10 nent reflecting damage to society if said subject contracts generating a score further comprises a fourth score compo
disease is endemic or pandemic . said pathogen . According to some embodiments of the invention , said According to some embodiments of the invention , said predicted likelihood of said subject of transmitting said
pathogen comprises one or more score components used for electronic positioning data comprises geographical location
generating a score . data .
According to some embodiments of the invention , said According to some embodiments of the invention , said
score relates to a predicted likelihood of a group of subjects physical proximity information comprises historical location
transmitting said pathogen based on said physical proximity data .
information , and said physical proximity information is a According to some embodiments of the invention , said
first score component used for said generating said score . 20 generating said score further comprises a component com
According to some embodiments of the invention , said prising historical health data .
generating said score further comprises a score component According to some embodiments of the invention , said
based on a nature of a location where said physical prox- generating said score further comprises a component com
imity information is related . prising a profession in record of said subject .
According to some embodiments of the invention , said 25 According to some embodiments of the invention , said
nature of the location is one or more of an open space , a physical proximity information further comprises informa
closed space , indoor , outdoor , ventilated indoor space , non- tion received from a third party .
ventilated indoor space and any combination thereof . According to some embodiments of the invention , said
According to some embodiments of the invention , when physical proximity information is provided by said subject
said physical proximity information is related to a location 30 actively .
that is either indoors or in a closed space , then said predicted According to some embodiments of the invention , said
likelihood of said subject of transmitting said pathogen physical proximity information is provided by said subject
increases by a factor of between about 10 times to about 100 passively by means of said one or more electronic devices .
times . According to some embodiments of the invention , said
According to some embodiments of the invention , said 35 pathogen is a virus .
physical proximity information is physical proximity data According to some embodiments of the invention , said
received by means of electronic positioning data of said virus is a corona virus .
subject . According to some embodiments of the invention , said
According to some embodiments of the invention , said virus is SARS – CoV .
physical proximity information is physical proximity data of 40 According to some embodiments of the invention , said
the location of said subject in relation to the location of other virus is MERS – CoV .
subjects . According to some embodiments of the invention , said
According to some embodiments of the invention , said virus is SARS – CoV – 2 .
physical proximity data comprises one or more of physical According to some embodiments of the invention , said
proximity distance data , duration of physical proximity data 45 virus is an influenza virus .
and / or ambience of physical proximity data . According to some embodiments of the invention , said
According to some embodiments of the invention , said disease results in influenza like symptoms .
electronic positioning data is one or more of electronic According to an aspect of some embodiments of the
geographical positioning data of said subject , electronic present invention there is provided a method of selecting
proximity positioning data of said subject relative to other 50 subjects for being vaccinated / treated against an infectious
subjects . disease caused by a pathogen , comprising :
According to some embodiments of the invention , said a . automatically collecting physical proximity informa
method further comprises generating a predicted likelihood tion of a subject with other subjects ;
of said subject contracting said pathogen based on said b . generating a predicted likelihood of said subject of
physical proximity data . 55 transmitting said virus based on said physical proximity
According to some embodiments of the invention , said information ;
generating a score further comprises a second score com- c . generating a score comprising a first score component
ponent based on said predicted likelihood of said subject based on said predicted likelihood of said subject of trans
contracting said pathogen based on said physical proximity mitting said virus ;
data . d . repeating steps b – c for a plurality of subjects ; and
According to some embodiments of the invention , said e . prioritizing vaccination / treatment of said subjects
electronic positioning data is collected using one or more according to said score .
electronic devices . According to some embodiments of the invention , said
According to some embodiments of the invention , said pathogen is selected from the group consisting of a virus , a
one or more electronic devices are one or more of a 65 bacterium , a fungus and a protozoan .
smartphone , a tablet , a smartwatch and a dedicated elec- According to some embodiments of the invention , said
tronic device . disease is endemic or pandemic .
60
US 11,107,588 B2
7 8
According to some embodiments of the invention , said According to some embodiments of the invention , said
generating said score further comprises a score component generating said score further comprises a component com
based on a nature of a location where said physical prox- prising a profession in record of said subject .
imity information is related . According to some embodiments of the invention , said
According to some embodiments of the invention , said 5 physical proximity information further comprises informa
nature of the location is one or more of an open space , a tion received from a third party .
closed space , indoor , outdoor , ventilated indoor space , non According to some embodiments of the invention , said
ventilated indoor space and any combination thereof . physical proximity information is provided by said subject According to some embodiments of the invention , when actively said physical proximity information is related to a location 10
that is either indoors or in a closed space , then said predicted According to some embodiments of the invention , said
likelihood of said subject of transmitting said pathogen physical proximity information is provided by said subject
increases by a factor of between about 10 times to about 100 passively by means of said one or more electronic devices .
times . According to some embodiments of the invention , said
According to some embodiments of the invention , said 15 pathogen is a virus .
physical proximity information is physical proximity data According to some embodiments of the invention , said
received by means of electronic positioning data of said virus is a corona virus .
subject . According to some embodiments of the invention , said
According to some embodiments of the invention , said virus is SARS – CoV .
physical proximity information is physical proximity data of 20 According to some embodiments of the invention , said
the location of said subject in relation to the location of other virus is MERS – CoV .
subjects . According to some embodiments of the invention , said
According to some embodiments of the invention , said virus is SARS – CoV – 2 .
physical proximity data comprises one or more of physical According to some embodiments of the invention , said
proximity distance data , duration of physical proximity data 25 virus is an influenza virus .
and / or ambience of physical proximity data . According to some embodiments of the invention , said
According to some embodiments of the invention , said disease results in influenza like symptoms .
electronic positioning data is one or more of electronic According to an aspect of some embodiments of the
geographical positioning data of said subject , electronic present invention there is provided a system for selecting
proximity positioning data of said subject relative to other 30 subjects for being vaccinated / treated against an infectious
subjects . disease caused by a pathogen , comprising :
According to some embodiments of the invention , said a . at least one server comprising a memory ;
method further comprises generating a predicted likelihood b . an analytics module ;
of said subject contracting said pathogen based on said c . a database module ;
physical proximity data . d . a simulation module ;
According to some embodiments of the invention , said said memory in said at least one server comprising instruc
generating a score further comprises a second score com- tions , said instructions comprising :
ponent based on said predicted likelihood of said subject i . generating , by circuitry , a predicted likelihood of said
contracting said pathogen based on said physical proximity subject of transmitting said pathogen based on said
data . physical proximity information , for a plurality of sub
According to some embodiments of the invention , said jects ;
electronic positioning data is collected using one or more ii . selecting subjects of said plurality of subjects for
electronic devices . vaccinating / treating based on a prediction that said
According to some embodiments of the invention , said vaccinating / treating said subjects will reduce a likeli
one or more electronic devices are one or more of a 45 hood of spreading of said disease in said plurality of
smartphone , a tablet , a smartwatch and a dedicated elec subjects , wherein said selecting is based on said gen
tronic device . erated predicted likelihood .
According to some embodiments of the invention , the According to some embodiments of the invention , said
method further comprising vaccinating / treating said subjects pathogen is selected from the group consisting of a virus , a
according to said score . 50 bacterium , a fungus and a protozoan .
According to some embodiments of the invention , said According to some embodiments of the invention , said
generating a score further comprises a third score compo- disease is endemic or pandemic .
nent reflecting relative health risk to said subject if said According to some embodiments of the invention , said
subject contracts said pathogen . predicted likelihood of said subject of transmitting said
According to some embodiments of the invention , said 55 pathogen comprises one or more score components used for
generating a score further comprises a fourth score compo- generating a score .
nent reflecting damage to society if said subject contracts According to some embodiments of the invention , said
said pathogen . score relates to a predicted likelihood of a group of subjects
According to some embodiments of the invention , said transmitting said pathogen based on said physical proximity
electronic positioning data comprises geographical location 60 information , and said physical proximity information is a
data . first score component used for said generating said score .
According to some embodiments of the invention , said According to some embodiments of the invention , said
physical proximity information comprises historical location generating said score further comprises a score component
data . based on a nature of a location where said physical prox
According to some embodiments of the invention , said 65 imity information is related .
generating said score further comprises a component com- According to some embodiments of the invention , said
prising historical health data . nature of the location is one or more of an open space , a
35
40
US 11,107,588 B2
9 10
closed space , indoor , outdoor , ventilated indoor space , non- According to some embodiments of the invention , said
ventilated indoor space and any combination thereof . physical proximity information is provided by said subject
According to some embodiments of the invention , when actively .
said physical proximity information is related to a location According to some embodiments of the invention , said
that is either indoors or in a closed space , then said predicted 5 physical proximity information is provided by said subject
likelihood of said subject of transmitting said pathogen passively by means of said one or more electronic devices .
increases by a factor of between about 10 times to about 100 According to some embodiments of the invention , said
times . simulation module further comprises a prediction module .
According to some embodiments of the invention , said According to some embodiments of the invention , said
physical proximity information is physical proximity data 10 pathogen is a virus .
received by means of electronic positioning data of said According to some embodiments of the invention , said
subject . virus is a corona virus .
According to some embodiments of the invention , said According to some embodiments of the invention , said
physical proximity information is physical proximity data of virus is SARS – CoV .
the location of said subject in relation to the location of other 15 According to some embodiments of the invention , said
subjects . virus is MERS – CoV .
According to some embodiments of the invention , said According to some embodiments of the invention , said
physical proximity data comprises one or more of physical virus is SARS – CoV – 2 .
proximity distance data , duration of physical proximity data According to some embodiments of the invention , said
and / or ambience of physical proximity data . 20 virus is an influenza virus .
According to some embodiments of the invention , said According to some embodiments of the invention , said
electronic positioning data is one or more of electronic disease results in influenza like symptoms .
geographical positioning data of said subject , electronic Following is a second non – exclusive list including some
proximity positioning data of said subject relative to other examples of embodiments of the invention . The invention
subjects . 25 also includes embodiments , which include fewer than all the
According to some embodiments of the invention , said features in an example , and embodiments using features
method further comprises generating a predicted likelihood from multiple examples , also if not expressly listed below .
of said subject contracting said pathogen based on said Example 1. A method of selecting subjects for being
physical proximity data . vaccinated against an infectious disease caused by a patho
According to some embodiments of the invention , said 30 gen , using personal physical proximity information of a
generating a score further comprises a second score com- subject , comprising :
ponent based on said predicted likelihood of said subject a . generating , by circuitry , a predicted likelihood of said
contracting said pathogen based on said physical proximity subject of transmitting said pathogen based on said physical
data . proximity information , for a plurality of subjects ;
According to some embodiments of the invention , said 35 b . selecting subjects of said plurality of subjects for
electronic positioning data is collected using one or more vaccinating based on a prediction that said vaccinating said
electronic devices . subjects will reduce a likelihood of spreading of said disease
According to some embodiments of the invention , said in said plurality of subjects , wherein said selecting is based
one or more electronic devices are one or more of a on said generated predicted likelihood .
smartphone , a tablet , a smartwatch and a dedicated elec- 40 Example 2. The method according to example 1 , wherein
tronic device . said pathogen is selected from the group consisting of a
According to some embodiments of the invention , the virus , a bacterium , a fungus and a protozoan .
system further comprising vaccinating / treating said subjects Example 3. The method according to according to any one
according to said score . of examples 1-2 , wherein said disease is endemic or pan
According to some embodiments of the invention , said 45 demic .
generating a score further comprises a third score compo- Example 4. The method according to any one of examples
nent reflecting relative health risk to said subject if said 1-3 , wherein said predicted likelihood of said subject of
subject contracts said pathogen . transmitting said pathogen comprises one or more score
According to some embodiments of the invention , said components used for generating a score .
generating a score further comprises a fourth score compo- 50 Example 5. The method according to example 4 , wherein
nent reflecting damage to society if said subject contracts said score relates to a predicted likelihood of a group of
said pathogen . subjects transmitting said pathogen based on said physical
According to some embodiments of the invention , said proximity information , and said physical proximity infor
electronic positioning data comprises geographical location mation is a first score component used for said generating
data . 55 said score .
According to some embodiments of the invention , said Example 6. The method according to any one of examples
physical proximity information comprises historical location 4-5 , wherein said generating said score further comprises a
data . score component based on a nature of a location where said
According to some embodiments of the invention , said physical proximity information is related .
generating said score further comprises a component com- 60 Example 7. The method of example 6 , wherein said nature
prising historical health data . of the location is one or more of an open space , a closed
According to some embodiments of the invention , said space , indoor , outdoor , ventilated indoor space , non – venti
generating said score further comprises a component com- lated indoor space and any combination thereof .
prising a profession in record of said subject . Example 8. The method according to any one of examples
According to some embodiments of the invention , said 65 1-7 , wherein when said physical proximity information is
physical proximity information further comprises informa- related to a location that is either indoors or in a closed
tion received from a third party . space , then said predicted likelihood of said subject of
5
15
25
US 11,107,588 B2
11 12
transmitting said pathogen increases by a factor of between Example 26. The method according to any one of
about 10 times to about 100 times . examples 1-25 , wherein said physical proximity information
Example 9. The method according to any one of examples is provided by said subject passively by means of said one
1-8 , wherein said physical proximity information is physical or more electronic devices .
proximity data received by means of electronic positioning Example 27. The method according to any one of
data of said subject . examples 1-26 , wherein said pathogen is a virus .
Example 10. The method according to any one of Example 28. The method according to any one of
examples 1-9 , wherein said physical proximity information examples 1-27 , wherein said virus is a corona virus .
is physical proximity data of the location of said subject in Example 29. The method according to any one of
relation to the location of other subjects . 10 examples 1-28 , wherein said virus is SARS – CoV .
Example 11. The method according to any one of Example 30. The method according to any one of examples 9-10 , wherein said physical proximity data com examples 1-28 , wherein said virus is MERS – CoV . prises one or more of physical proximity distance data , Example 31. The method according to any one of duration of physical proximity data and / or ambience of examples 1-28 , wherein said virus is SARS – CoV – 2 . physical proximity data . Example 32. The method according to any one of examples 1-27 , wherein said virus is an influenza virus .
Example 12. The method according to any one of Example 33. The method according to any one of examples 9-11 , wherein said electronic positioning data is examples 1-32 , wherein said disease results in influenza like
one or more of electronic geographical positioning data of symptoms .
said subject , electronic proximity positioning data of said 20 Example 34. A method of selecting subjects for being
subject relative to other subjects . vaccinated against an infectious disease caused by a patho
Example 13. The method according to any one of gen , comprising :
examples 1-12 , wherein said method further comprises a . automatically collecting physical proximity informa
generating a predicted likelihood of said subject contracting tion of a subject with other subjects ;
said pathogen based on said physical proximity data . b . generating a predicted likelihood of said subject of
Example 14. The method according to any one of transmitting said virus based on said physical proximity
examples 4-13 , wherein said generating a score further information ;
comprises a second score component based on said pre c . generating a score comprising a first score component
dicted likelihood of said subject contracting said pathogen based on said predicted likelihood of said subject of trans
based on said physical proximity data . 30 mitting said virus ;
Example 15. The method according to any one of d . repeating steps b – c for a plurality of subjects; and examples 9-14 , wherein said electronic positioning data is e . prioritizing vaccination of said subjects according to collected using one or more electronic devices . said score . Example 16. The method of example 15 , wherein said one Example 35. The method according to example 34 , 35 wherein said pathogen is selected from the group consisting
or more electronic devices are one or more of a smartphone , of a virus, a bacterium , a fungus and a protozoan . a tablet, a smartwatch and a dedicated electronic device . Example 36. The method according to any one of Example 17. The method according to any one of examples 34-35 , wherein said disease is endemic or pan examples 4-16 , further comprising vaccinating said subjects demic .
according to said score . Example 37. The method according to any one of
Example 18. The method according to any one of examples 34-36 , wherein said generating said score further
examples 4-17 , wherein said generating a score further comprises a score component based on a nature of a location
comprises a third score component reflecting relative health where said physical proximity information is related .
risk to said subject if said subject contracts said pathogen . Example 38. The method according to any one of
Example 19. The method according to any one of 45 examples 34-37 , wherein said nature of the location is one
examples 4-18 , wherein said generating a score further or more of an open space , a closed space , indoor , outdoor ,
comprises a fourth score component reflecting damage to ventilated indoor space , non – ventilated indoor space and any
society if said subject contracts said pathogen . combination thereof .
Example 20. The method according to any one of Example 39. The method according to any one of
examples 9-19 , wherein said electronic positioning data 50 examples 34-38 , wherein when said physical proximity
comprises geographical location data . information is related to a location that is either indoors or
Example 21. The method according to any one of in a closed space , then said predicted likelihood of said
examples 1-20 , wherein said physical proximity information subject of transmitting said pathogen increases by a factor of
comprises historical location data . between about 10 times to about 100 times . Example 40. The
Example 22. The method according to any one of 55 method according to any one of examples 34-39 , wherein
examples 4-21 , wherein said generating said score further said physical proximity information is physical proximity
comprises a component comprising historical health data . data received by means of electronic positioning data of said
Example 23. The method according to any one of subject
examples 4-22 , wherein said generating said score further Example 41. The method according to any one of
comprises a component comprising a profession in record of 60 examples 34-40 , wherein said physical proximity informa
said subject . tion is physical proximity data of the location of said subject
Example 24. The method according to any one of in relation to the location of other subjects .
examples 1-23 , wherein said physical proximity information Example 42. The method according to any one of
further comprises information received from a third party . examples 38-41 , wherein said physical proximity data com
Example 25. The method according to any one of 65 prises one or more of physical proximity distance data ,
examples 1-24 , wherein said physical proximity information duration of physical proximity data and / or ambience of
is provided by said subject actively . physical proximity data .
40
US 11,107,588 B2
13 14
Example 43. The method according to any one of Example 64. The method according to any one of
examples 38-42 , wherein said electronic positioning data is examples 1-63 , wherein said disease results in influenza like
one or more of electronic geographical positioning data of symptoms .
said subject , electronic proximity positioning data of said Example 65. A system for selecting subjects for being
subject relative to other subjects . 5 vaccinated against an infectious disease caused by a patho Example 44. The method according to any one of gen , comprising :
examples 38-43 , wherein said method further comprises generating a predicted likelihood of said subject contracting a . at least one server comprising a memory ; said pathogen based on said physical proximity data . b . an analytics module; Example 45. The method according to any one of 10 c . a database module ; examples 34-44 , wherein said generating a score further d . a simulation module ; comprises a second score component based on said pre said memory in said at least one server comprising instruc
dicted likelihood of said subject contracting said pathogen tions , said instructions comprising :
based on said physical proximity data . i . generating , by circuitry , a predicted likelihood of said
Example 46. The method according to any one of is subject of transmitting said pathogen based on said physical
examples 38-45 , wherein said electronic positioning data is proximity information , for a plurality of subjects ;
collected using one or more electronic devices . Example 47 . ii . selecting subjects of said plurality of subjects for
The method according to example 46 , wherein said one or vaccinating based on a prediction that said vaccinating said
more electronic devices are one or more of a smartphone , a subjects will reduce a likelihood of spreading of said disease
tablet , a smartwatch and a dedicated electronic device . 20 in said plurality of subjects , wherein said selecting is based
Example 48. The method according to any one of on said generated predicted likelihood .
examples 34-47 , further comprising vaccinating said sub- Example 66. The system according to example 65 ,
jects according to said score . wherein said pathogen is selected from the group consisting
Example 49. The method according to any one of of a virus , a bacterium , a fungus and a protozoan .
examples 34-48 , wherein said generating a score further 25 Example 67. The system according to any one of
comprises a third score component reflecting relative health examples 65-66 , wherein said disease is endemic or pan
risk to said subject if said subject contracts said pathogen . demic .
Example 50. The method according to any one of Example 68. The system according to any one of
examples 34-49 , wherein said generating a score further examples 65-67 , wherein said predicted likelihood of said
comprises a fourth score component reflecting damage to 30 subject of transmitting said pathogen comprises one or more
society if said subject contracts said pathogen . score components used for generating a score .
Example 51. The method according to any one of Example 69. The system according to example 68 ,
examples 38-50 , wherein said electronic positioning data wherein said score relates to a predicted likelihood of a
comprises geographical location data . group of subjects transmitting said pathogen based on said
Example 52. The method according to any one of 35 physical proximity information , and said physical proximity
examples 34-51 , wherein said physical proximity informa- information is a first score component used for said gener
tion comprises historical location data . ating said score .
Example 53. The method according to any one of Example 70. The system according to any one of
examples 34-52 , wherein said generating said score further examples 64-69 , wherein said generating said score further
comprises a component comprising historical health data . 40 comprises a score component based on a nature of a location
Example 54. The method according to any one of where said physical proximity information is related .
examples 34-53 , wherein said generating said score further Example 71. The system of example 70 , wherein said
comprises a component comprising a profession in record of nature of the location is one or more of an open space , a
said subject . closed space , indoor , outdoor , ventilated indoor space , non
Example 55. The method according to any one of 45 ventilated indoor space and any combination thereof .
examples 34-54 , wherein said physical proximity informa- Example 72. The system according to any one of
tion further comprises information received from a third examples 65-71 , wherein when said physical proximity
party . information is related to a location that is either indoors or
Example 56. The method according to any one of in a closed space , then said predicted likelihood of said
examples 34-55 , wherein said physical proximity informa- 50 subject of transmitting said pathogen increases by a factor of
tion is provided by said subject actively . between about 10 times to about 100 times .
Example 57. The method according to any one of Example 73. The system according to any one of
examples 34-56 , wherein said physical proximity informa- examples 65-72 , wherein said physical proximity informa
tion is provided by said subject passively by means of said tion is physical proximity data received by means of elec
one or more electronic devices . 55 tronic positioning data of said subject .
Example 58. The method according to any one of Example 74. The system according to any one of
examples 34-57 , wherein said pathogen is a virus . examples 65-73 , wherein said physical proximity informa
Example 59. The method according to any one of tion is physical proximity data of the location of said subject
examples 34-58 , wherein said virus is a corona virus . in relation to the location of other subjects .
Example 60. The method according to any one of 60 Example 75. The system according to any one of
examples 34-58 , wherein said virus is SARS – CoV . examples 69-74 , wherein said physical proximity data com
Example 61. The method according to any one of prises one or more of physical proximity distance data ,
examples 34-58 , wherein said virus is MERS – CoV . duration of physical proximity data and / or ambience of
Example 62. The method according to any one of physical proximity data .
examples 34-58 , wherein said virus is SARS – CoV – 2 . Example 76. The system according to any one of
Example 63. The method according to any one of examples 69-75 , wherein said electronic positioning data is
examples 1-57 , wherein said virus is an influenza virus . one or more of electronic geographical positioning data of
65
US 11,107,588 B2
15 16
said subject , electronic proximity positioning data of said Example 97. The system according to any one of
subject relative to other subjects . examples 65-91 , wherein said virus is an influenza virus .
Example 77. The system according to any one of Example 98. The system according to any one of
examples 65-76 , wherein said method further comprises examples 65-92 wherein said disease results in influenza like
generating a predicted likelihood of said subject contracting 5 symptoms .
said pathogen based on said physical proximity data . Unless otherwise defined , all technical and / or scientific
Example 78. The system according to any one of terms used herein have the same meaning as commonly
examples 64-77 , wherein said generating a score further understood by one of ordinary skill in the art to which the
comprises a second score component based on said pre invention pertains . Although methods and materials similar
dicted likelihood of said subject contracting said pathogen 10 or equivalent to those described herein can be used in the
based on said physical proximity data . practice or testing of embodiments of the invention , exem Example 79. The system according to any one of plary methods and / or materials are described below . In case
examples 69-78 , wherein said electronic positioning data is of conflict , the patent specification , including definitions ,
collected using one or more electronic devices . will control. In addition, the materials, methods, and Example 80. The system according to example 79 , 15 examples are illustrative only and are not intended to be necessarily limiting .
wherein said one or more electronic devices are one or more As will be appreciated by one skilled in the art , some of a smartphone , a tablet , a smartwatch and a dedicated embodiments of the present invention may be embodied as
electronic device . a system , method or computer program product . Accord
Example 81. The system according to any one of 20 ingly , some embodiments of the present invention may take
examples 64-80 , further comprising vaccinating said sub- the form of an entirely hardware embodiment , an entirely
jects according to said score . software embodiment ( including firmware , resident soft
Example 82. The system according to any one of ware , micro – code , etc. ) or an embodiment combining soft
examples 64-81 , wherein said generating a score further ware and hardware aspects that may all generally be referred
comprises a third score component reflecting relative health 25 to herein as a “ circuit , ” “ module ” or “ system . ” Furthermore ,
risk to said subject if said subject contracts said pathogen . some embodiments of the present invention may take the
Example 83. The system according to any one of form of a computer program product embodied in one or
examples 64-82 , wherein said generating a score further more computer readable medium ( s ) having computer read
comprises a fourth score component reflecting damage to able program code embodied thereon . Implementation of the
society if said subject contracts said pathogen . 30 method and / or system of some embodiments of the inven
Example 84. The system according to any one of tion can involve performing and / or completing selected
examples 69-83 , wherein said electronic positioning data tasks manually , automatically , or a combination thereof . comprises geographical location data . Moreover , according to actual instrumentation and equip
Example 85. The system according to any one of ment of some embodiments of the method and / or system of
examples 65-84 , wherein said physical proximity informa- 35 the invention , several selected tasks could be implemented
tion comprises historical location data . by hardware , by software or by firmware and / or by a
Example 86. The system according to any one of combination thereof , e.g. , using an operating system .
examples 64-85 , wherein said generating said score further For example , hardware for performing selected tasks
comprises a component comprising historical health data . according to some embodiments of the invention could be
Example 87. The system according to any one of 40 implemented as a chip or a circuit . As software , selected
examples 64-86 , wherein said generating said score further tasks according to some embodiments of the invention could
comprises a component comprising a profession in record of be implemented as a plurality of software instructions being
said subject . executed by a computer using any suitable operating system .
Example 88. The system according to any one of In an exemplary embodiment of the invention , one or more
examples 65-87 , wherein said physical proximity informa- 45 tasks according to some exemplary embodiments of method
tion further comprises information received from a third and / or system as described herein are performed by a data
party . processor , such as a computing platform for executing a
Example 89. The system according to any one of plurality of instructions . Optionally , the data processor
examples 65-88 , wherein said physical proximity informa- includes a volatile memory for storing instructions and / or
tion is provided by said subject actively . 50 data and / or a non – volatile storage , for example , a magnetic
Example 90. The system according to any one of hard – disk and / or removable media , for storing instructions
examples 65-89 , wherein said physical proximity informa- and / or data . Optionally , a network connection is provided as
tion is provided by said subject passively by means of said well . A display and / or a user input device such as a keyboard
one or more electronic devices . or mouse are optionally provided as well .
Example 91. The system according to any one of 55 Any combination of one or more computer readable
examples 65-90 , wherein said simulation module further medium ( s ) may be utilized for some embodiments of the
comprises a prediction module . invention . The computer readable medium may be a com
Example 92. The system according to any one of puter readable signal medium or a computer readable stor
examples 65-91 , wherein said pathogen is a virus . age medium . A computer readable storage medium may be ,
Example 93. The system according to any one of 60 for example , but not limited to , an electronic , magnetic ,
examples 65-92 , wherein said virus is a corona virus . optical , electromagnetic , infrared , or semiconductor system ,
Example 94. The system according to any one of apparatus , or device , or any suitable combination of the
examples 65-92 , wherein said virus is SARS – CoV . foregoing . More specific examples ( a non – exhaustive list ) of
Example 95. The system according to any one of the computer readable storage medium would include the
examples 65-92 , wherein said virus is MERS – CoV . 65 following : an electrical connection having one or more
Example 96. The system according to any one of wires , a portable computer diskette , a hard disk , a random
examples 65-91 , wherein said virus is SARS – CoV – 2 . access memory ( RAM ) , a read – only memory ( ROM ) , an
in any
US 11,107,588 B2
17 18
erasable programmable read – only memory ( EPROM or ratus , or other devices to cause a series of operational steps
Flash memory ) , an optical fiber , a portable compact disc to be performed on the computer , other programmable
read – only memory ( CD – ROM ) , an optical storage device , a apparatus or other devices to produce a computer imple
magnetic storage device , or any suitable combination of the mented process such that the instructions which execute on foregoing . In the context of this document , a computer 5 the computer or other programmable apparatus provide
readable storage medium may be any tangible medium that processes for implementing the functions / acts specified in
can contain , or store a program for use by or in connection the flowchart and / or block diagram block or blocks .
with an instruction execution system , apparatus , or device . Some of the methods described herein are generally
A computer readable signal medium may include a propa- designed only for use by a computer , and may not be feasible
gated data signal with computer readable program code 10 or practical for performing purely manually , by a human
embodied therein , for example , in baseband or as part of a expert . A human expert who wanted to manually perform
carrier wave . Such a propagated signal may take any of a similar tasks might be expected to use completely different
variety of forms , including , but not limited to , electro ds , e.g. , making use of expert knowledge and / or the
magnetic , optical , or any suitable combination thereof . A pattern recognition capabilities of the human brain , which
computer readable signal medium may be any computer 15 would be vastly more efficient than manually going through
readable medium that is not a computer readable storage the steps of the methods described herein .
medium and that can communicate , propagate , or transport
a program for use by or in connection with an instruction BRIEF DESCRIPTION OF THE SEVERAL
execution system , apparatus , or device . VIEWS OF THE DRAWINGS
Program code embodied on a computer readable medium 20
and / or data used thereby may be transmitted using any Some embodiments of the invention are herein described ,
appropriate medium , including but not limited to wireless , by way of example only , with reference to the accompanying
wireline , optical fiber cable , RF , etc. , or any suitable com- drawings . With specific reference now to the drawings in
bination of the foregoing . detail , it is stressed that the particulars shown are by way of
Computer program code for carrying out operations for 25 example and for purposes of illustrative discussion of
some embodiments of the present invention may be written embodiments of the invention . In this regard , the description
combination of one or more programming languages , taken with the drawings makes apparent to those skilled in
including an object oriented programming language such as the art how embodiments of the invention may be practiced .
Java , Smalltalk , C ++ or the like and conventional procedural In the drawings :
programming languages , such as the “ C ” programming 30 FIG . 1 is a schematic illustration of an exemplary defi
language or similar programming languages . The program nition of a superspreader , according to some embodiments
code may execute entirely on the user’s computer , partly on of the invention ;
the user’s computer , as a stand – alone software package , FIG . 2 is a flowchart of an exemplary embodiment of the
partly on the user’s computer and partly on a remote invention , according to some embodiments of the invention ;
computer or entirely on the remote computer or server . In the 35 FIG . 3 is a schematic flowchart of a method of calculating
latter scenario , the remote computer may be connected to the a weighted score , according to some embodiments of the
user’s computer through any type of network , including a invention ;
local area network ( LAN ) or a wide area network ( WAN ) , or FIG . 4 is a schematic representation of an exemplary
the connection may be made to an external computer ( for spreading network , according to some embodiments of the
example , through the Internet using an Internet Service 40 invention ;
Provider ) . FIGS . 5a – f are flowcharts of exemplary methods for
Some embodiments of the present invention may be identifying superspreaders with high levels of anonymiza
described below with reference to flowchart illustrations tion , according to some embodiments of the invention ;
and / or block diagrams of methods , apparatus ( systems ) and FIG . 6 is a flowchart of a method of generating a score ,
computer program products according to embodiments of 45 according to some embodiments of the invention ; and
the invention . It will be understood that each block of the FIG . 7 is a schematic representation of an exemplary
flowchart illustrations and / or block diagrams , and combina- system , according to some embodiments of the invention .
tions of blocks in the flowchart illustrations and / or block
diagrams , can be implemented by computer program DESCRIPTION OF SPECIFIC EMBODIMENTS
instructions . These computer program instructions may be 50 OF THE INVENTION
provided to a processor of a general purpose computer ,
special purpose computer , or other programmable data pro- The present invention , in some embodiments thereof ,
cessing apparatus to produce a machine , such that the relates to methods and systems of prioritizing vaccination /
instructions , which execute via the processor of the com- treatment and , more particularly , but not exclusively , to
puter or other programmable data processing apparatus , 55 methods and systems of prioritizing vaccination / treatment in
create means for implementing the functions / acts specified a pandemic situation .
in the flowchart and / or block diagram block or blocks . Overview
These computer program instructions may also be stored A broad aspect of some embodiments of the invention
in a computer readable medium that can direct a computer , relates to reduce a pandemic by reducing a k value of
other programmable data processing apparatus , or other 60 infection in addition to and / or at the expense of reducing an
devices to function in a particular manner , such that the RO value thereof . In some embodiments of the invention ,
instructions stored in the computer readable medium pro- this is achieved by identifying and vaccinating ( or otherwise
duce an article of manufacture including instructions which preventing infection by ) persons who are potential super
implement the function / act specified in the flowchart and / or spreaders ( e.g. , people who , on the average , are expected to
block diagram block or blocks . 65 infect more than the average , for example , 1 , 2 , 3 or more or
The computer program instructions may also be loaded intermediate values of standard deviations from such aver
onto a computer , other programmable data processing appa- age . This may result in effective lowering of RO and / or of
9 US 11,107,588 B2
19 20
effective herd immunity . Optionally , people are not mea- that person to spread the disease . In some embodiments ,
sured by actual spreading , but rather by characteristics potential superspreaders are identified according to a score
and / or behavior , which is expected to lead to greater spread- in relation to other scores from the rest of the population . In
ing than others . Optionally , such considerations also may be some embodiments , potential superspreaders are identified
applied to below average in expected spreading , however , 5 according to a score in relation to a predetermined score
such people usually have a smaller overall effect on disease generated by the system . In some embodiments , identified
spread . potential superspreaders having the highest score are vac
A broad aspect of some embodiments of the invention cinated ( or provided with prophylactic treatments ) first . It
relates to using a prediction of individual behavior to decide should be appertained that the score may also be weighted
on vaccination priority for that individual . In some embodi- 10 with other information , such as criticality for infrastructure ,
ments of the invention , such prediction is based on past social standing and / or risk form the disease or perceived risk
behavior of the individual . In some embodiments of the to high – value members of society .
invention , an individual is given a score used for prioriti- An aspect of some embodiments of the invention relates
zation . In some embodiments of the invention , actual pri- to prioritizing vaccinations and / or prophylactic treatments in
oritization may be based on a determination of the expected 15 a pandemic event according to a potential level of danger to
effect of such vaccination on spread of disease . Optionally , the society . In some embodiments , the invention relates
this determination is using a simulation of population dis- identification of individuals that , in case they were in a phase
ease spread . In some embodiments of the invention , how- of infecting others with an infectious disease / virus / patho
ever , people are evaluated as individuals . gen , it would potentially put everyone else in danger . For
A broad aspect of some embodiments of the invention 20 example , in the case where a subject is in potential contact
relates to soft – fail of vaccination prioritization , which avoids with other people and those other people potentially meet a
problems caused by imprecise automated tracking methods . high number of individuals . For example , a subject that
In some embodiments of the invention , the use of imperfect interacts face to face with health provider personnel , but
information , which , on the one hand does not seriously does not belong to the health provides network . If that
damage the quality of scoring and , on the other hand , can be 25 subject becomes infected , he / she can potentially infect a
used to significantly increase privacy and / or ease of score high number of health provider personnel , which will then ,
collection is provided . It is noted that a mistake , for potentially , spread the infectious disease / virus / pathogen to a
example , of 4 % , 8 % , 15 % or intermediate percentages in larger population .
score of an individual or missing a potential super spreader An aspect of some embodiments of the invention relates
will not have a significantly ( e.g. , a factor of 2 or more ) 30 to protecting the privacy of individuals in a population when
greater effect on a person ( e.g. , will not send such person their information is used for prioritizing vaccinations and / or
into quarantine ) and / or the total efficacy of a vaccination prophylactic treatments in a pandemic event , optionally also
process . Also , even after such an effect , it is expected that the according to a potential level of danger to the society . In
overall result is better than naïve or general classification- some embodiments , actual names of individuals are
based vaccination prioritization . In some embodiments of 35 encrypted and / or anonymized in the system . In some
the invention , counting of contacts is allowed to be less embodiments , only a device of an individual comprises the
precise . In some embodiments of the invention , identifica- capabilities to translate between the actual name of the
tion of the quality of the contacts ( e.g. , indoor / outdoor , individual and the encrypted / anonymized user name . In
coughing behavior , actual proximity and / or existence of some embodiments , the servers of the system comprise high
protective factors ) is allowed to be reduced and optionally 40 levels of protection and / or encryption for the information
carried out using less precise sensing means as provided , for stored therein . In some embodiments of the invention , even
example , by cellphones . Optionally or additionally , the the device of the user stores a minimum of identifiable
identification of unique contacts is allowed to be less pre- information , such as a score , but does not stores actual
cise . identities of persons met .
An aspect of some embodiments of the invention relates 45 In some embodiments of the invention , private informa
to prioritizing vaccinations and / or prophylactic treatments in tion about a person’s activity and / or persons they came in
a pandemic event by identifying potential superspreaders . In contact with and / or geolocations are maintained on that
some embodiments , potential superspreaders are identified person’s mobile device and used to determine a priority for
from a population before critical groups have been excluded . that person ( e.g. , by assessing the number of contacts and
In some embodiments , potential superspreaders are identi- 50 overall risk of spreading disease due to typical behavior of
fied from a population after critical groups have been that person ) . Optionally , the mobile device is used to broad
excluded . In some embodiments , critical groups are for cast , optionally in an anonymous manner , the score , so that ,
example , health care providers , essential service provides it may be determined , for example , by a central computer ,
and high – risk individuals . In some embodiments , potential the distribution of scores across the population . It should be
superspreaders are identified according to one or more of : 55 noted that the actual identification of the device and / or user
their usual and / or expected level of activity , their usual is not needed , just the number of persons with each score , so
and / or expected type of activity , their usual and / or expected this can be taken into account together with number and / or
health state , their belonging to a closed or open circle of availability of vaccine doses , to plan a best dosing schedule .
connections , the kind of individuals a certain subject usually Optionally , the mobile device will receive a predetermined
meets , the kind of individuals a certain subject has met and 60 scale of scores from the system , which will be then used by
their actual sensed behavior . In some embodiments , the the mobile device to translate the score in view of the scale
entire population ( with or without the critical groups ) , or a of scores and communicate the user to get treatment and ,
part of the population , such as a critical group or other optionally , the when and where .
group , are subjected to an analysis which provides each In some embodiments of the invention , once calculated ,
individual with a “ superspreader score ” ( referred hereinafter 65 such dosing schedule is broadcasted and each device can
just as “ score ” ) which reflects a likelihood of such a person apply its score to the schedule to determine a priority , which
acting as a superspreader and / or general expected ability of is given to the device owner . Optionally , when arriving for
US 11,107,588 B2
21 22
a scheduled vaccination , the device owner is required to complete or semi or otherwise restrictions ) , the total risk of
show that code and , optionally , proof that the telephone spread may be reduced with a same or smaller number of
belongs to them . vaccine doses .
In one example , the local device calculates a score based A potential benefit of some embodiments of the invention
on a user’s medical information and behavior . Optionally is 5 is self – policing . If a person does not install suitable software
also receives behavior of those that person meets ( e.g. , for tracking movements , such person may receive a lower
transmitted to the device at proximity / contact of devices of priority of treatment / vaccination . Similarly , if a person
those people ) . In some embodiments , the information is leaves their device off , then such off – time can be noted and
stored without identification of source , except possibly a used to affect the score , or even can be used as an indication
hash code , which , while can be used to detect that a certain 10 that that person is not at risk .
device was “ met ” , it cannot be used to identify the device . In some embodiments of the invention , a process of using the method includes : In some embodiments, once this score ( e.g. , risk of conta ( a ) Learning the behavior of individuals . This may be gion ) is calculated , the broadcasted information regarding done , for example , using existing contact tracking methods
number of vaccinations available and / or number of persons 15 and / or using methods as discussed herein . Optionally , such in each class is noted . In some embodiments , this data may learned behavior is maintained in privacy and / or collected in be used to determine which vaccination priority the personal an anonymous manner or processed as it is collected , to
device score merits , for example , in the same manner as preserve anonymity .
would be by a central computer ( e.g. , all scores above x , ( b ) Scoring , which can be based , for example , on number ,
where there are y people with a score above x and y is the 20 variety and / or quality of contacts , degree of bridging
number of available vaccines ) . between subpopulations , risk to individual , risk to others the
In some embodiments of the invention , broadcasts and individual is in contact with , other facts that affect spreading
data transmissions are digitally signed to prevent tampering . ( e.g. , chronic cough ) and / or existing immunity .
This has a potential advantage of allowing more anonymous ( c ) Inviting the individual to be vaccinated , optionally
transmission method to be used ( e.g. , Tor ) . 25 though software on an electronic device used for contact
It should be noted that additionally or alternatively to a tracking .
central processing , the calculation of the vaccine priority ( d ) Vaccination , optionally verified using the software to
may be distributed between some or all of the mobile identify the person being vaccinated .
devices , for example , using parallelization methods known An aspect of some embodiments of the invention relates
in the art , which optionally also prevent significant amount 30 to identifying potential superspreaders without the use of
of information from passing through any particular device . personal data . In some embodiments , superspreaders are
In some embodiments of the invention , the device calcu- identified by providing an anonymous ID to each individual ,
lates the priority and determines when the device owner for example , when a dedicated application / software ( re
should be vaccinated , treated and / or tested . For example , the ferred hereinafter as “ application ” or “ app ‘ ) is installed in an
number and duration of persons in proximity to the device 35 electronic device . In some embodiments , IDs are exchanged
can be used to calculate a risk score . Optionally , medical between electronic devices when in proximity to each other
information , such as susceptibility and / or risk of spreading ( e.g. , to indicate a potentially infectious “ meeting ” of the
by coughing is downloaded to the device . This is typically device holders ) . In some embodiments , what is transmitted
not a significant breach of anonymity , as the identity of the is only a part of such ID ( or an indication thereof ) , which
device is typically known to the medical record provider . In 40 potentially decreases the chances to identify the specific
some embodiments of the invention , a person can apply to user . In some embodiments of the invention , even the partial
receive a rating , for example , based on importance , job ( e.g. , IDs substantially unique ( e.g. , a random number with more
healthcare provider ) , being part of critical infrastructure possibilities than the number of expected meetings ) . In some
and / or risk of death . Such rating may be provided in the form embodiments of the invention , the partial ID is selected to be
of a one – time code , which the person can enter into the 45 non – unique , for example , including only 100 , 1000 , 10,000
device . In this manner , the device can increase or decrease or intermediate or smaller or greater possibilities . In some
the risk score and / or priority of vaccination , without any embodiments , prioritizing vaccinations and / or prophylactic
central authority being aware of the person’s activities . treatments in a pandemic event is performed according to a
In some embodiments of the invention , as the device superspreader score calculated by the number of IDs col
calculates the person’s score , it may generate warning to the 50 lected by each user .
device owner to avoid or reduce certain behavior . Option- An aspect of some embodiments of the invention relates
ally , such warning is tied to reduction in priority if not to the quality of people an individual meets . In some
heeded . Optionally or additionally , the manually entered embodiments of the invention , meeting with a person can be
rating may affect such warnings . For example , socially given a higher or lower weight , based on whether that person
promiscuuoo us activity by a doctor may not merit such warn- 55 is himself a super spreader and / or tends to meet super
ing and / or may not reduce the doctor’s score ( at least while spreaders and / or tends to meet others form many sub
activity is performed at an allowed location , such as a populations . In some embodiments of the invention , when
hospital , which location may be indicated as part of the two devices meet , they exchange their own score and / or
rating ) , but will generate a warning or a sanction ( e.g. , if not number of contacts or other information , which is used to
heeded ) to a person without such rating . 60 generate an indication of how much of a potential super
In some embodiments of the invention , when deciding if spreader that person is . In some embodiments , such people
to allow entry of a person into a crowded location , such as may be given a higher weight . Optionally or additionally ,
a sports arena or a shopping mall , a user may be required to persons who are from a same subpopulation and / or which
show their rating . have fewer contacts and / or which are met more often , are
A potential benefit of some embodiments of the invention 65 given a lower weight .
is that rather than give out vaccination to critical workers , An aspect of some embodiments of the invention relates
while placing the rest of society in a lockdown ( e.g. , to assessing the degree of contacts inside a subpopulation
9
5
10
15
US 11,107,588 B2
23 24
and between subpopulations . Society often has bubbles ensure that the probability of a same second ID reaching an
( subpopulations ) within which there is a lot of contact within individual from two different subgroups is sufficiently low
the bubble but considerably less contact between bubbles . In ( e.g. , below 10 % ) .
such a context , a person who bridges between bubbles may An aspect of some embodiments of the invention relates
be a greater threat of disease spread than a person with more to the political issues involved in vaccination prioritization .
overall contacts but most or all within the bubble . In some In some embodiments of the invention , using an objective
embodiments of the invention , a method is provided for measure of risk due to behavior allows vaccination selection
assessing the degree to which a person is within bubbles without ( or less ) a political fiat of selecting groups and / or
and / or bridges between bubbles or between non – bubble reducing political pressure applied to prefer a particular
subpopulations . For example , the method may be used to group , as the individuals are treated by prioritization soft
distinguish between a first person where 90 % of their ware as individuals and do are not identified as or treated as
contacts are within a strongly connected sub – group vs. a belonging to particular groups . Also within a particular
person where only 10 % of their contacts are to a same group , using an automated vaccination prioritization method
strongly sub – group vs a person where 90 % of contacts are to can be used to reduce friction and argument .
a strongly connected sub – group , but there are multiple An aspect of some embodiments of the invention relates
( exclusive ) such subgroups . to encouraging users to use a dedicated application / software
In some embodiments of the invention , a distributed for tracking contacts ( and optionally identifying potential
method of assessing the degree to which contacts of an superspreaders either anonymized or not ) by providing
individual are within a strongly connected or other type of 20 vaccinations and / or prophylactic treatments first to those
bubble , is provided . An alternative view of such method is individuals that use the dedicated software . In some embodi
assessing a degree of diffusion , which may be correlated ments , individuals that use the dedicated software are those
with a degree of propagation of disease . individuals that contribute to the overall benefit of the
In some embodiments of the invention , some or all population , therefore are provided with vaccinations and / or
individuals are assigned a second ( or more ) ID which is 25 prophylactic treatments before those who not .
transferred to people they meet at a probability lower than Before explaining at least one embodiment of the inven
100 % . Optionally , when two individuals meet they exchange tion in detail , it is to be understood that the invention is not
not only their second ID , but also all second IDs they have necessarily limited in its application to the details of con
collected . As with a regular ID , the second or further IDs struction and the arrangement of the components and / or
may be more or less unique . When an individual device 30 methods set forth in the following description and / or illus
assesses the second IDs it collected , it will tend to have trated in the drawings and / or the Examples . The invention is
fewer IDs if it is within a bubble ( e.g. , because it will mainly capable of other embodiments or of being practiced or
have IDs within the bubble ) than if it interconnects bubbles carried in various ways .
( e.g. , in which case it can have IDs from multiple bubbles ) . Definition of the Population
Optionally , the number of second IDs is used as a measure 35 During a pandemic , once a valid vaccine / prophylactic
of diffusion of IDs in the contact network . In some embodi- drug becomes available , and the number of vaccines / drug
ments of the invention , the transfer of second IDs can be doses is limited or not all available at the same time , the
weighted ( and / or probability of transfer adjusted ) , for government must decide who will receive first the vaccine /
example , to better model the likely of transfer of disease , for prophylactic treatment . According to studies , governments
example , weighted higher for IDs collected in closed spaces , 40 decide to provide the first doses of the treatment to the group
at close distances or IDs received from a device owned by of individuals that belong to :
a person with a chronic cough and / or less if owner is known a ) Health care services , for example doctors , nurses ,
( e.g. , recorded as such ) to be careful with facemasks or other laboratories , hospitals , etc .;
protective gear . Such weighting may be used additionally or b ) Essential service services , for example police , fire
alternatively also for the other scores described herein . The 45 fighters , public sector personnel , governmental personnel ,
score may be normalized to the period in which the score is
collected . Such normalization may be alternatively or addi- c ) High risk individuals , for example people with high
tionally applied to score based on the first ID . The normal- risk of complications , pregnant women , children , etc.
ization may be non – linear ( e.g. , the score may increase faster These individuals belong to a group called critical groups ,
at early times ) and may be different for different IDs and / or 50 due to the nature of their activity or due to their health status
for different individual characteristic values . during pandemic times . Usually , critical groups amount to
In some embodiments of the invention , the probability of about 2 % to about 10 % of the total population of a country .
transfer is preset ( e.g. , 0.01 % , 0.1 % , 1 % , 10 % or interme- After the critical groups have been vaccinated and / or
diate or smaller or greater percentages ) . Optionally or addi- provided prophylactic treatments , since the number of vac
tionally , multiple additional IDs are provided , each one 55 cinations / treatments is limited , there is the question who
transferred at a different probability . Optionally , the preset should be vaccinated / treated next . This is generally true also
probability is determined using a simulation . It is noted that within a critical group or other group chosen for vaccination ,
with a very small transfer probability , there may not be for example , a group of less at risk individuals , such as
sufficient diffusion of second ID values , while with a large males aged 50-60 .
probability , all individuals will collect all second IDs , given 60 In some embodiments , the population is defined as a
enough time . For example , a simulation of a contact network number of individuals between about 10 individuals and
may be run with different preset transfer values to detect a about 100 individuals , optionally between about 100 indi
value which allows to distinguish between typical sub- viduals and about 1,000 individuals , optionally between
population sizes and / or which , within the measurement about 1,000 individuals and about 1,000,000 individuals ,
period , does not reflect diffusion of substantially all second 65 optionally up to 10,000,000 , optionally up to 100,000,000 ,
IDs all over the network . Similarly , the degree of uniqueness optionally up to the entire population of earth ( e.g. , 8
of the second ID may be selected using such a simulation to billion ) .
etc .; and
9
10
US 11,107,588 B2
25 26
Principals of Herd Immunity demics with such superspreader events ( SSEV ) , the majority
Before explaining the invention , the notion of herd immu- of individuals infect relatively few secondary contacts .
nity should be explained . Herd immunity ( also called herd Although loose definitions of superspreader events exist ,
effect , community immunity , population immunity , or social some effort has been made at defining what qualifies as a
immunity ) is a form of indirect protection from infectious 5 superspreader event ( SSEV ) . Lloyd – Smith et al . ( 2005 )
disease that occurs when a large percentage of a population define a protocol to identify a superspreader event as fol
has become immune ( resistant ) to an infection , whether lows :
through vaccination / prophylactic treatment or previous a . estimate the effective reproductive number , R , for the
infections , thereby providing a measure of protection for disease and population in question ;
individuals who are not immune . In a population in which a b . construct a Poisson distribution with mean R , repre
large proportion of individuals possess immunity , such senting the expected range of Z due to stochasticity without
people being unlikely to contribute to disease transmission , individual variation ;
chains of infection are more likely to be disrupted , which c . define an SSEV as any infected person who infects
either stops or substantially slows the spread of disease . The 15 more than Z ( n ) others , where Z ( n ) is the nth percentile of the
greater the proportion of immune individuals in a commu- Poisson ( R ) distribution .
nity , the smaller the probability that non – immune individuals This protocol defines a 99th – percentile SSEV as a case ,
will come into contact with an infectious individual , helping which causes more infections than would occur in 99 % of
to shield non – immune individuals from infection . Individu- infectious histories in a homogeneous population . For
als can become immune by recovering from an earlier 20 example , during the SARS – CoV – 1 2002-2004 SARS out
infection or through vaccination / prophylactic treatment . break from China , epidemiologists defined a superspreader
Some individuals cannot become immune because of medi- as an individual with at least eight transmissions of the
cal conditions , such as an immunodeficiency or immuno- disease . Furthermore , superspreaders may or may not show
suppression , and for this group herd immunity is a crucial any symptoms of the disease . In the methods described here ,
method of protection . Once a certain threshold has been 25 a threshold ( or scale ) for being a superspreader may be
reached , herd immunity gradually eliminates a disease from defined manually and / or determined by analyzing actual
a population . This elimination , if achieved worldwide , may result in the permanent reduction in the number of infections contact -t ransmission data collected manually and / or auto matically .
to zero , called eradication . For example , herd immunity Putting aside hospitals , private residences and old – age created via vaccination / treatment contributed to the eventual 30 homes , almost all of these superspreader events ( SSEVs ) eradication of smallpox in 1977 and has contributed to the took place in the context of ( 1 ) parties , ( 2 ) face – to – face reduction of the frequencies of other diseases. Herd immu professional networking events and meetings, ( 3 ) religious nity does not apply to all dis ses , just those that are
contagious , meaning that they can be transmitted from one gatherings , ( 4 ) sports events , ( 5 ) meat – processing facilities ,
individual to another . Tetanus , for example , is infectious but 35 ( 6 ) ships at sea , ( 7 ) singing groups , and ( 8 ) funerals . not contagious, so herd immunity does not apply . Herd Factors of Transmission
immunity was recognized as a naturally occurring phenom Superspreaders have been identified who excrete a higher
enon in the 1930s when it was observed that after a signifi than normal number of pathogens during the time they are
cant number of children had become immune to measles , the infectious . This causes their contacts to be exposed to higher
number of new infections temporarily decreased , including 40 viral / bacterial loads than would be seen in the contacts of
among susceptible children . Mass vaccination / treatment to non – superspreaders with the same duration of exposure .
induce herd immunity has since become common and This medical information may be available for at least some
proved successful in preventing the spread of many infec- individuals , for example , if the epidemic is a recurring one ,
tious diseases . One of the main problems with achieving such as influenza . In addition , behavioral and medical attri
herd immunity is that there might be a limited number of 45 butes may also increase infectivity . For example , a chronic
vaccinations / treatments available to the population and mass cough ( or one due to a temporary disease , which may be
vaccination / treatment is either not possible or it would take noted in a person’s medical record ) may increase the degree
a long time to achieve herd immunity while the infectious to which an individual is contagious . It is noted that coughs
disease continues to spread . and sneezes ( and rate thereof ) can be detected automatically
It is a potential benefit of some embodiments of the 50 by a carried device , such as a cellphone , by signal analysis
invention to provide a method to resolve the problem of who on an automatically and optionally continually ( or repeat
to vaccinate / treat during a pandemic when a low amount of edly discrete ) collected audio signal form the microphone . It
vaccine / treatment doses are available , while still providing is noted that an individual’s cellphone or other electronic
an effective herd immunity , optionally by better targeting device may have access to a person medical records , by
those individuals likely to pass on disease and vaccinating at 55 connecting to an EMR of that individual .
least some of them , in a preferential manner . Basic Reproductive Number
Definition of Superspreaders The basic reproduction number RO is the average number
A superspreader is an unusually contagious organism of secondary infections caused by a typical infective person
infected with a disease ( infectious disease / virus / pathogen ) . in a totally susceptible population . The basic reproductive
In the context of a human – borne illness , a superspreader is 60 number is found by multiplying the average number of
an individual who is more likely to infect others , compared contacts by the average probability that a susceptible indi
with a typical infected person . vidual will become infected , which is called the shedding
Some cases of superspreading conform to the 80/20 rule , potential . The average number of contacts may further be
where approximately 20 % of infected individuals are weighed by quality of contact ( e.g. , length , repetition , dis
responsible for 80 % of transmissions , although superspread- 65 tance , protective means and / or airflow quality )
ing can still be said to occur when superspreaders account
for a higher or lower percentage of transmissions . In epi RO = Number of contactsxShedding potential
10
US 11,107,588 B2
27 28
Individual Reproductive Number In some embodiments of the invention , the disease is
The individual reproductive number represents the num- transmitted by respiratory means , for example , aerosol and /
ber of secondary infections caused by a specific individual or droplets . Optionally , an electronic device , such as a
during the time that individual is infectious . Some individu cellphone is used to detect contact which may be sufficient
als have significantly higher than average individual repro- 5 to transmit ( e.g. , detecting proximity for example , using
ductive numbers and are known as superspreaders . Through Bluetooth power ; detecting physical activity for example ,
contact tracing, epidemiologists have identified super buy analysis of an audio trace recorded from such device ; spreaders in measles , tuberculosis, rubella, monkeypox , detecting being indoors or outdoors based on geolocation or based on other sensors on the cellphone that are affected by smallpox , Ebola hemorrhagic fever and SARS . being indoors ( e.g. , echoes in audio ) .
Co – Infections with Other Pathogens Vaccinations and Prophylactic Treatments
Studies have shown that men with HIV who are co- In some embodiments , the term vaccination means the
infected with at least one other sexually transmitted disease , administration of a vaccine to help the immune system
such as gonorrhea , hepatitis C , and herpes simplex 2 virus , develop protection from a disease . In some embodiments ,
have a higher HIV shedding rate than men without co vaccines contain a microorganism or virus in a weakened ,
infection . This shedding rate was calculated in men with 15 live or killed state , or proteins or toxins from the organism .
similar HIV viral loads . Once treatment for the co – infection In some embodiments, in stimulating the body’s adaptive has been completed , the HIV shedding rate returns to levels immunity, they help prevent sickness from an infectious disease . In some embodiments , as stated above , when a comparable to men without co – infection . Therefore , it could sufficiently large percentage of a population has been vac
be hypothesized that in case of viral diseases transmitted 20 cinated , herd immunity results .
through fluids , people with other pathologies , like chronic In some embodiments , the term prophylactic treatment
coughing , could also be defined as superspreaders and are means a preventive measure taken to fend off a disease or
optionally so defined , or weighted accordingly in some another unwanted cons uence .
embodiments of the invention . In order to facilitate the explanation of the invention , the
Exemplary Pathogens 25 term ” treatment ” will be used . It should be understood that
In some embodiments , a pathogen may be one or more of when the term ” treatment ” is used it refers to both vacci
a virus ( in pl . viruses ) , bacterium ( bacteria ) , fungus ( fungi ) nations and prophylactic treatment .
a protozoan ( protozoa ) , for example coronavirus In some embodiments , vaccines are all compounds as
( COVID – 19 , SARS – CoV – 1 , SARS – CoV – 2 , MERS – CoV ) . In disclosed in in the website of the World Health Organization
some embodiments , the pathogen may be a virus , and said 30 ( https : // www [ dot ] who [ dot ] int / publications / m / item / draft
virus is an influenza virus . In some embodiments , the landscape – of – covid – 19 – candidate – vaccines ) , which are all
disease results in influenza like symptoms . It should be incorporated herein by reference , and which are optionally
understood , that where referred “ virus ” and / or ” patho- provided ( e.g. , as a kit ) with software such as described
gen ” , any one of an “ infectious disease ” , a “ generic or herein and / or provided with instructions for use targeting
specific pathogen ” , a ” generic or specific virus ” are 35 potential super spreaders detected , for example , using meth
included , and the use of the term “ virus ” and / or “ pathogen ” ods and apparatus as described herein , and include the
is just to facilitate the explanation and they should include following :
them . 28 candidate vaccines in clinical evaluation
or
Type of
COVID – 19
Vaccine
developer /
manufacturer
Vaccine
platform
Route
of
Admin
istration
Number
of
doses
candidate
vaccine
Timing
of
doses
Clinical
Stage
Phase 1 Phase 1/2 Phase 2 Phase 3
University ChAdOxl – S 1 IM 2020-001228-32
of Oxford /
AstraZeneca
Non
Replicating
Viral
Vector
ISR
CTN
89951424
PACTR
202006922165132
2020-001072-15
Interim
Report
NCT04383574
NCT04352608
Chi
CTR
2000031809
Sinovac Inactivated Inactivated 2 IM NCT
04456595
Chi
CTR
Inactivated Inactivated
0 , 14
days
0 , 14 or
0 , 21
days
2 IM
2000034780
Inactivated Inactivated 2 0 , 14 or IM
Wuhan
Institute of
Biological
Products /
Sinopharm
Beijing
Institute of
Biological
Products /
Sinopharm
Moderna /
NIAID
0 , 21
Chi
CTR
2000032459
Chi
CTR
days 2000034780
RNA LNP 2 IM NCT04405076 NCT04470427
encapsulated
mRNA
0 , 28
days
NCT
04283461
Interim
Report
BioNTech / RNA 2 IM
FosunPharma /
Pfizer
3 LNP
mRNAs
0 , 28
days
NCT
04368728
2020-001038-36
Chi
CTR
2000034825
US 11,107,588 B2
29 30
-continued
COVID – 19
Vaccine
developer /
manufacturer
Type of
candidate
vaccine
Vaccine
platform
Number
of
doses
Route
of
Admin
istration
Timing
of
doses
Clinical
Stage
Phase 1 Phase 1/2 Phase 2 Phase 3
Non Adenovirus 1 IM
Replicating
Viral
Vector
Type 5
Chi
CTR
2000030906
Study Report
Chi
CTR
2000031781
Study Report
Vector
Protein 2 or 3 IM
Subunit
0 , 28
or
0 , 28 ,
NCT
04445194
Adjuvanted
recombinant
protein
( RBD
Dimer )
NCT
04466085
56 days
Inactivated Inactivated 2 0 , 28 IM
days
NCT
04412538
NCT
04470609
CanSino
Biological
Inc./Beijing
Institute of
Biotechnology
Anhui
Zhifei
Longcom
Bio
pharmaceutical /
Institute of
Microbiology ,
Chinese
Academy
of
Sciences
Institute of
Medical
Biology ,
Chinese
Academy
of Medical
Sciences
Inovio
Pharma
ceuticals /
International
Vaccine
Institute
Osaka
University /
AnGes /
Takara Bio
Cadila
Healthcare
Limited
Genexine
Consortium
DNA 2 0 , 28 ID
days
NCT
04447781
NCT
04336410
DNA 2 0 , 14 IM
days
NCT
04463472
DNA
plasmid
vaccine
with
electro
poration
DNA
plasmid
vaccine +
Adjuvant
DNA
plasmid
vaccine
DNA
Vaccine
( GX – 19 )
Whole
Virion
Inactivated
Ad26COVS1
DNA 3 0 , 28 , ID
56 days
CTRI /
2020/07/026352
DNA 2 0 , 28 IM
days
NCT
04445389
Bharat Inactivated 2 IM
Biotech
0 , 14
days
NCT
04471519
2 0 , 56 IM
days
NCT
04436276
Janssen
Pharma
ceutical
Companies
Novavax
Non
Replicating
Viral
Vector
Protein
Subunit
2 0 , 21 IM
days
NCT
04368988
Full
length
recombinant
SARS
COV – 2
glycoprotein
nanoparticle
vaccine
adjuvanted
with
Matrix M
RBD
based
Protein 2 IM
Subunit
0 , 21
days
NCT
04473690
RNA mRNA IM
Kentucky
Bioprocessing ,
Inc
Arcturus /
Duke – NUS
Gamaleya
Research
Institute
NCT
04480957
Adeno 1 IM
based
Non
Replicating
Viral
Vector
Protein
Subunit
NCT
04436471
NCT
04437875
NCT
04405908
2 0 , 21 IM
days
Clover
Biopharma
ceuticals Inc./
GSK / Dynavax
Native
like
Trimeric
subunit
Spike
Protein
vaccine
US 11,107,588 B2
31 32
-continued
COVID – 19
Vaccine
developer /
manufacturer
Type of
candidate
vaccine
Vaccine
platform
Number
of
doses
Route
of
Admin
istration
Timing
of
doses
Clinical
Stage
Phase 1 Phase 1/2 Phase 2 Phase 3
Vaxine Pty 1 IM
Ltd / Medytox
Protein
Subunit
NCT
04453852
Protein 2 0 , 28 IM
Subunit
ACTRN
days 12620000674932p
University
of
Queensland /
CSL / Seqirus
Recombinant
spike
protein
with
Advax TM
adjuvant
Molecular
clamp
stabilized
Spike
protein
with
MF59
adjuvant
Measles
vector
based
Replicating 1 or 2 0 , 28 IM
Viral
Vector
days
NCT
04497298
( not yet
recruiting )
Institute
Pasteur /
Themis /
Univ . of
Pittsburg
CVR / Merck
Sharp &
Dohme
Imperial
College
London
Curevac
RNA LNP 2 IM
nCoVsaRNA
ISRCTN
17072692
RNA mRNA 2 0 , 28 IM
days
0 , 14
or 0 .
RNA mRNA 2 IM
NCT
04449276
Chi
CTR
28 days 2000034112
People’s
Liberation
Army
( PLA )
Academy
of Military
Sciences /
Walvax
Biotech .
Medicago
Inc.
VLP 2 0,21 IM
days
NCT
04450004
Plant
derived
VLP
adjuvanted
with GSK
or
Dynavax
adjs .
S – 2P
protein +
CpG1018
Protein 2
Subunit
0 28 IM
days
NCT
04487210
Medigen
Vaccine
Biologics
Corporation
NIAID /
Dynavax
139 candidate vaccines in preclinical evaluation
Type of for non
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Coronavirus candidate candidates
candidate
Platform vaccine
Coronavirus
Developer target
DNA DIOSynVax Ltd Pre – Clinical
University of
Cambridge
SARS – CoV – 2
and
SarbecoCoronaviruses
DNA ,
engineered
vaccine inserts
compatible
with multiple
delivery
systems
DNA vaccine
DNA plasmid
vaccine
RBD & N
DNA
DNA
SARS – CoV2
SARS – CoV2
Pre – Clinical
Pre – Clinical
Ege University
Scancell / University
of Nottingham /
Nottingham Trent
University
US 11,107,588 B2
33 34
-continued
Type of for non
Current stage of Same platform
clinical evaluation /
regulatory status- Coronavirus
Coronavirus candidate candidates
candidate
vaccine
Coronavirus
Platform Developer target
DNA National Research SARS – CoV2 Pre – Clinical
Centre , Egypt
DNA plasmid
vaccine
S , S1 , S2 ,
RBD & N
DNA with
electroporation
DNA SARS – CoV2 Pre – Clinical
DNA DNA with SARS – CoV2 Pre – Clinical
electroporation
DNA DNA SARS – CoV2 Pre – Clinical
DNA Plasmid DNA , SARS – CoV2 Pre – Clinical SARS
Needle – Free
Delivery
Karolinska
Institute / Cobra
Biologics
( OPENCORONA
Project )
Chula Vaccine
Research Center
Takis / Applied
DNA
Sciences / Evvivax
Immunomic
Therapeutics ,
Inc./EpiVax ,
Inc./PharmaJet
BioNet Asia
Mediphage
Bioceuticals / University
of Waterloo
Entos
Pharmaceuticals
Symvivo
KM Biologics
DNA
DNA
DNA vaccine
msDNA
vaccine
SARS – CoV2
SARS – CoV2
Pre – Clinical
Pre – Clinical
DNA DNA vaccine SARS – CoV2 Pre – Clinical
DNA
Inactivated
SARS – CoV2
SARS – CoV2
Pre – Clinical
Pre – Clinical JE , Zika
bacTRL – Spike
Inactivated +
alum
Inactivated
Inactivated
Inactivated
Inactivated
SARS – CoV2
SARS – CoV2
Pre – Clinical
Pre – Clinical
Inactivated Inactivated SARS – CoV2 Pre – Clinical
whole virus
Inactivated Inactivated SARS – CoV2 Pre – Clinical
Selcuk University
Erciyes
University
National Research
Centre , Egypt
Beijing Minhai
Biotechnology
Co. , Ltd.
Osaka University /
BIKEN /
NIBIOHN
Sinovac / Dynavax
Inactivated TBD SARS – CoV2 Pre – Clinical
Inactivated SARS – CoV2 Pre – Clinical
Inactivated
Inactivated +
CpG 1018
Inactivated +
CpG 1018
Inactivated
Valneva / Dynavax SARS – CoV2 Pre – Clinical
Inactivated Research Institute SARS – CoV2 Pre – Clinical
for Biological
Safety Problems ,
Rep of
Live SARS – CoV2 Pre – Clinical
Attenuated
Virus
Codon
deoptimized
live attenuated
vaccines
Kazakhstan
Mehmet Ali
Aydinlar
University /
Acibadem
Labmed
Health
Services A.S.
Codagenix / Serum
Institute of
India
Live SARS – CoV2 Pre – Clinical
Attenuated
Virus
Codon
deoptimized
live attenuated
vaccines
HAV ,
InfA ,
ZIKV ,
FMD ,
SIV , RSV ,
DENV
Live SARS – CoV2 Pre – Clinical
Attenuated
Virus
Codon
deoptimized
live attenuated
vaccines
Sendai virus
Indian
Immunologicals
Ltd / Griffith
University
ID Pharma SARS – CoV2 Pre – Clinical
vector
Non
Replicating Viral
Vector
Non
Replicating Viral
Vector
Adenovirus SARS – CoV2 Pre – Clinical
based
Ankara
University
US 11,107,588 B2
35 36
-continued
Type of for non
candidate
vaccine
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Coronavirus candidate candidates
Coronavirus
Platform Developer target
Non SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
Adeno
associated
virus vector
( AAVCOVID )
Massachusetts
Eye and
Ear / Massachusetts
General
Hospital / AveXis
Non Geo Vax / Bravo Vax SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
MVA encoded
VLP
LASV ,
EBOV ,
MARV ,
HIV
Non SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
ReiThera /
LEUKOCARE /
Univercells
Replication
defective
Simian
Adenovirus
( GRAD )
encoding
SARS – CoV – 2S
MVA – S
encoded
Non SARS – CoV2 Pre – clinical Many
replicating viral
vector
DZIF
German
Center for
Infection
Research / IDT
Biologika
GmbH
IDIBAPS
Hospital
Clinic , Spain
Altimmune
Non MVA – S SARS – CoV2 Pre – clinical
replicating viral
vector
Non
Replicating Viral
Vector
adenovirus SARS – CoV2 Pre – Clinical influenza
based
NasoVAX
expressing
SARS2 – COV
spike protein
Adeno 5 – based Erciyes SARS – CoV2 Pre – Clinical
University
Non
Replicating Viral
Vector
Non
Replicating Viral
Vector
Immunity Bio SARS – CoV2 Pre – Clinical
Inc. &
NantKwest ,
Inc.
2nd Gen E2b
Ad5 Spike ,
RBD ,
Nucleocapsid
Subcutaneous &
Oral
Ad5 S
( GREVAX TM
platform )
Oral Ad5 S
flu , Chik ,
Zika ,
EBOV ,
LASV ,
HIV / SIV ,
Cancer
Non Greffex SARS – CoV2 Pre – Clinical MERS
Replicating Viral
Vector
Non
Replicating Viral
Vector
Stabilitech SARS – CoV2 Pre – Clinical
Biopharma
Ltd
Zika ,
VZV ,
HSV – 2
and
Norovirus
Valo Pan -C orona Pre – Clinical
Therapeutics
Ltd
Non adenovirus
Replicating Viral based + HLA
Vector matched
peptides
Non Oral Vaccine
Replicating Viral platform
Vector
Vaxart SARS – CoV2 Pre – Clinical InfA ,
CHIKV ,
LASV ,
NORV ;
EBOV ,
RVF ,
HBV ,
VEE
Multiple
candidates
Non SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
MVA
expressing
structural
proteins
Dendritic cell
based vaccine
Centro
Nacional
Biotecnologia
( CNB – CSIC ) , Spain
University of
Manitoba
Non SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
Non
Replicating Viral
Vector
parainfluenza SARS – CoV2 Pre – Clinical MERS
virus 5 ( PIV5 )
based vaccine
expressing the
spike protein
University of
Georgia /
University
of Iowa
US 11,107,588 B2
37 38
-continued
Type of for non
Current stage of Same platform
clinical evaluation /
regulatory status- Coronavirus
Coronavirus candidate candidates
candidate
vaccine
Coronavirus
Platform Developer target
Non SARS – CoV2 Pre – Clinical
Replicating Viral
Vector
Recombinant
deactivated
rabies virus
containing si
Bharat
Biotech / Thomas
Jefferson
University
HeV , NIV ,
EBOV ,
LASSA ,
CCHFV ,
MERS
Influenza A SARS – CoV2 Pre – Clinical
H1N1 vector
Non
Replicating Viral
Vector
Non
Replicating Viral
Vector
Inactivated SARS – CoV2 Pre – Clinical
Flu – based
SARS – CoV2
vaccine +
Adjuvant
National
Research
Centre , Egypt
National
Center for
Genetic
Engineering and
Biotechnology
( BIOTEC ) / GPO ,
Thailand
Research
Institute for
Biological
Safety
Problems ,
Protein Protein Subunit SARS – CoV2 Pre – Clinical
Subunit
Rep of
Kazakhstan
Protein RBD -p rotein Mynvax SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
Recombinant S SARS – CoV2 Pre – Clinical
protein
Izmir
Biomedicine
and Genome
Center
Bogazici
University
University of
Virginia
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
SARS – CoV2 Pre – Clinical
Peptide +
novel adjuvant
S subunit
intranasal
liposomal
formulation
with
GLA / 3M052
adjs .
S – Protein
( Subunit ) +
Adjuvant ,
E coli based
Expression
Protein SARS – CoV2 Pre -C linical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein Subunit
S , N , M & si
protein
Helix Biogen
Consult ,
Ogbomoso &
Trinity
Immonoefficient
Laboratory ,
Ogbomoso ,
Oyo State ,
Nigeria .
National
Research
Centre ,
Egypt
University of
San Martin
and
CONICET ,
Argentina
Chulalongkorn
University / GPO ,
Thailand
AdaptVac
( PREVENT
nCov
consortium )
Expres2ion
Protein Protein Subunit SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
RBD protein
fused with Fc
of IgG + Adj .
Capsid – like
Particle
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Drosophila S2
insect cell
expression
system VLPs
Peptide
antigens
formulated in
LNP
Protein IMV Inc SARS – CoV2 Pre – Clinical
Subunit
US 11,107,588 B2
39 40
-continued
Type of for non
candidate
vaccine
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Coronavirus candidate candidates
Coronavirus
Platform Developer target
Protein S protein SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
S protein + SARS – CoV2 Pre – Clinical Influenza
Adjuvant
Protein SARS – CoV2 Pre – Clinical
Subunit
VLP
recombinant
protein +
Adjuvant
WRAIR
USAMRIID
National
Institute of
Infectious
Disease ,
Japan / Shionogi
UMN Pharma
Osaka
University /
BIKEN /
National
Institutes of
Biomedical
Innovation ,
Japan
Univ . of
Pittsburgh
Protein SARS – CoV2 Pre – Clinical MERS
Subunit
microneedle
arrays S1
subunit
Protein Peptide Vaxil Bio SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
Biological E SARS – CoV2 Pre – Clinical
Ltd
Adjuvanted
protein subunit
( RBD )
Protein Peptide SARS – CoV2 Pre – Clinical
Subunit
Flow Pharma
Inc
Ebola ,
Marburg ,
HIV , Zika ,
Influenza ,
HPV
therapeutic
vaccine ,
BreastCA
vaccine
Protein S protein AJ Vaccines SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
Il – Key peptide Generex / EpiVax SARS – CoV2 Pre – Clinical Influenza ,
HIV ,
SARS – CoV
S protein EpiVax /U niv. SARS – CoV2 Pre – Clinical H7N9
of Georgia
EpiVax SARS – CoV2
Protein
Subunit
Protein
Subunit
Protein
Subunit
Pre – Clinical
SARS – CoV2 Pre – Clinical
Protein Subunit
EPV – CoV – 19
S protein
( baculovirus
production )
gp – 96
backbone
Sanofi
Pasteur / GSK
Influenza ,
SARS – CoV
Protein SARS – CoV2 Pre – Clinical
Subunit
Heat
Biologics / Univ .
Of Miami
NSCLC ,
HIV ,
malaria ,
Zika
Protein SARS – CoV2 Pre – Clinical Ebola
Subunit
Peptide
vaccine
Protein SARS – CoV2 Pre – Clinical
Subunit
Subunit
vaccine
FBRI SRC
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
FBRI SRC
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
Baylor
College of
Medicine
iBio / CC
Pharming
Protein SARS – CoV2 Pre – Clinical SARS
Subunit
S1 or RBD
protein
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Subunit
protein , plant
produced
Recombinant
protein ,
nanoparticles
( based on S
protein and
other epitopes )
Saint
Petersburg
scientific
research
institute of
vaccines and
serums
US 11,107,588 B2
41 42
-continued
Type of for non
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Coronavirus candidate candidates
candidate
vaccine
Coronavirus
Platform Developer target
Protein SARS – CoV2 Pre – Clinical HPV
Subunit
Innovax / Xiamen
Univ./GSK
COVID – 19
XWG – 03
truncated S
( spike ) proteins
Adjuvanted
microsphere
peptide
Protein SARS – CoV2 Pre – Clinical
Subunit
VIDO
InterVac ,
University of
Saskatchewan
Protein OncoGen SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Synthetic Long
Peptide
Vaccine
candidate for S
and M proteins
Oral E. coli
based protein
expression
system of s
and N proteins
Nanoparticle
vaccine
Plant – based
subunit
( RBD – Fc +
Adjuvant )
MIGAL
Galilee
Research
Institute
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
SARS – CoV2 Pre – Clinical
Protein SARS – CoV2 Pre – Clinical
Subunit
OMV – based
vaccine
LakePharma ,
Inc.
Baiya
Phytopharm /
Chula
Vaccine
Research
Center
Quadram
Institute
Biosciences
BIOMVIS
Srl / Univ . of
Trento
Lomonosov
Moscow
State
University
Flu A ,
plague
Protein SARS – CoV2 Pre – Clinical
Subunit
OMV – based
vaccine
Protein SARS – CoV2 Pre – Clinical
subunit
rubella ,
rotavirus
structurally
modified
spherical
particles of the
tobacco mosaic
virus ( TMV )
Protein Spike – based SARS – CoV2 Pre – Clinical Hepatitis C
Subunit
Protein
Subunit
University of
Alberta
AnyGo
Technology
SARS – CoV2 Pre – Clinical
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein
Subunit
Recombinant
S1 – Fc fusion
protein
Recombinant
protein
Recombinant
protein in IC
BEVS
Orally
delivered , heat
stable subunit
Yisheng
Biopharma
Vabiotech SARS – CoV2 Pre – Clinical
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Peptides
derived from
Spike protein
Protein Protein Subunit SARS – CoV2 Pre – Clinical
Subunit
Applied
Biotechnology
Institute ,
Inc.
Axon
Neuroscience
SE
MOGAM
Institute for
Biomedical
Research , GC
Pharma
Neovii / Tel
Aviv
University
Intravacc / Epivax
Protein RBD -b ased SARS – CoV2 Pre – Clinical
Subunit
Protein SARS – CoV2 Pre – Clinical
Subunit
Protein Intravacc / Epivax SARS – CoV2 Pre – Clinical
Subunit
Outer
Membrane
Vesicle ( OMV )
subunit
Outer
Membrane
Vesicle ( OMV )
peptide
Spike – based
( epitope
screening )
Protein SARS – CoV2 Pre -C linical
Subunit
ImmunoPrecise /
LiteVax
BV
US 11,107,588 B2
43 44
-continued
Type of
Current stage of Same platform
clinical evaluation / for non
regulatory status- Coronavirus
Coronavirus candidate candidates
candidate
vaccine
Coronavirus
Platform Developer target
Replicating Viral YF17D Vector KU Leuven SARS – CoV2 Pre -C linical
Vector
Replicating Viral
Vector
Measles Vector SARS – CoV2 Pre – Clinical
Replicating Viral Measles Vector SARS – CoV2 Pre – Clinical
Vector
SARS – CoV2
Cadila
Healthcare
Limited
FBRI SRC
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
DZIF
German
Center for
Infection
Research
Can Virex AG
Tonix
Pharma / Southern
Research
Replicating Viral
Vector
Measles Virus Pre – clinical
( S , N targets )
Zika ,
H7N9 ,
CHIKV
SARS – CoV2 Pre – Clinical Smallpox ,
monkeypox
BIOCAD and SARS – CoV2 Pre – Clinical Influenza
IEM
FBRI SRC SARS – CoV2 Pre – Clinical Influenza
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
SARS – CoV2 Pre – Clinical Influenza
Replicating Viral Horsepox
Vector vector
expressing S
protein
Replicating Viral Live viral
Vector vectored
vaccine based
on attenuated
influenza virus
backbone
( intranasal )
Replicating Viral Recombinant
Vector vaccine based
on Influenza A
virus , for the
prevention of
COVID – 19
( intranasal )
Replicating Viral Attenuated
Vector Influenza
expressing an
antigenic
portion of the
Spike protein
Replicating Viral Influenza
Vector vector
expressing
RBD
Replicating Viral Replication
Vector competent
VSV chimeric
virus
technology
( VSVAG )
delivering the
SARS – CoV2
Spike ( S )
glycoprotein .
Replicating Viral VSV – S
Vector
Fundação
Oswaldo
Cruz and
Instituto
Buntantan
University of SARS – CoV2 Pre – Clinical
Hong Kong
IAVI / Merck SARS – CoV2 Pre – Clinical Ebola ,
Marburg ,
Lassa
SARS – CoV2 Pre – Clinical HIV ,
MERS
University of
Western
Ontario
Replicating Viral VSV – S Aurobindo SARS – CoV2 Pre – Clinical
Vector
Replicating Viral
Vector
VSV vector SARS – CoV2 Pre – Clinical
VSV – S
FBRI SRC
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
Israel
Institute for
Biological
Research / Weizmann
Institute of
Science
Replicating Viral SARS – CoV2
Vector
Pre – Clinical
US 11,107,588 B2
45 46
-continued
Type of for non
candidate
vaccine
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Coronavirus candidate candidates
Coronavirus
Platform Developer target
Replicating Viral SARS – CoV2 Pre – Clinical influenza
Vector
UW
Madison / FluGen /
Bharat
Biotech
Replicating Viral SARS – CoV2 Pre – Clinical
Vector
M2 – deficient
single
replication
( M2SR )
influenza
vector
Newcastle
disease virus
vector ( NDV
SARSCOV
2 / Spike )
Avian
paramyxovirus
vector
( APMV )
Self
amplifying
RNA
mRNA
Intravacc /
Wageningen
Bioveterinary
Research
Utrecht Univ .
The
Lancaster
University ,
UK
Gennova
Replicating Viral SARS – CoV2 Pre – Clinical
Vector
RNA SARS – CoV2 Pre – Clinical
RNA SARS – CoV2 Pre – Clinical
RNA LNP – mRNA SARS – CoV2 Pre – Clinical
RNA LNP – mRNA SARS – CoV2 Pre – Clinical
RNA LNP SARS – CoV2 Pre – Clinical
encapsulated
mRNA
cocktail
encoding VLP
Selcuk
University
Translate
Bio / Sanofi
Pasteur
CanSino
Biologics / Precision
NanoSystems
Fudan
University /
Shanghai
Jiao Tong
University /
RNACure
Biopharma
Fudan
University /
Shanghai
Jiao Tong
University /
RNACure
Biopharma
Centro
Nacional Biotecnologia
( CNB – CSIC ) , Spain
University of
Tokyo / Daiichi
Sankyo
BIOCAD
RNA LNP SARS – CoV2 Pre – Clinical
encapsulated
mRNA
encoding RBD
RNA SARS – CoV2 Pre – Clinical
RNA SARS – CoV2 Pre – Clinical MERS
Replicating
Defective
SARS – CoV – 2
derived RNAS
LNP
encapsulated
mRNA
Liposome
encapsulated
mRNA
Several mRNA
candidates
mRNA
RNA SARS – CoV2 Pre – Clinical
RNA RNAimmune , Inc. SARS – CoV2 Pre – Clinical
RNA SARS – CoV2 Pre – Clinical
RNA mRNA SARS – CoV2 Pre – Clinical
FBRI SRC
VB
VECTOR ,
Rospotrebnadzor ,
Koltsovo
China
CDC / Tongji
University / Stermina
Chula
Vaccine
Research
Center / University
of Pennsylvania
eTheRNA
RNA LNP – mRNA SARS – CoV2 Pre – Clinical
RNA mRNA in an SARS – CoV2 Pre – Clinical
intranasal
delivery
system
RNA mRNA SARS – CoV2 Pre – Clinical
RNA mRNA
Greenlight
Biosciences
IDIBAPS
Hospital
Clinic , Spain
SARS – CoV2 Pre – Clinical
US 11,107,588 B2
47 48
-continued
Type of for non
candidate
vaccine
Current stage of Same platform
clinical evaluation
regulatory status- Coronavirus
Platform Coronavirus candidate candidates
Coronavirus
Developer target
VLP VLP SARS – CoV2 Pre – Clinical
VLP VLP SARS – CoV2 Pre – Clinical
Bezmialem
Vakif
University
Middle East
Technical
University
VBI
Vaccines Inc.
VLP SARS -C oV – 2 , Pre – Clinical
SARS – CoV , &
MERS – CoV
CMV ,
GBM , Zika
Enveloped
Virus – Like
Particle
( EVLP )
S protein
integrated in
HIV VLPs
VLP SARS – CoV2 Pre – Clinical
VLP VLP + SARS – CoV2 Pre – Clinical
Adjuvant
IrsiCaixa
AIDS
Research / IRTA
CReSA Barcelona
Supercomputing
Centre / Grifols
Mahidol
University / The
Government
Pharmaceutical
Organization
( GPO ) / Siriraj
Hospital
Navarrabiomed , Oncoimmunology
group
VLP SARS – CoV2 Pre – Clinical
VLP Saiba GmbH SARS – CoV2 Pre – Clinical
Virus – like
particles ,
lentivirus and
baculovirus
vehicles
Virus – like
particle , based
on RBD
displayed on
virus – like
particles
ADDomerTM
multiepitope
display
VLP Imophoron SARS – CoV2 Pre – Clinical
Ltd and
Bristol
University’s
Max Planck
Centre
Doherty
Institute
OSIVAX
VLP Unknown SARS – CoV2 Pre – Clinical
VLP VLP SARS – CoV1 Pre – Clinical
SARS – CoV2
VLP eVLP ARTES SARS – CoV2 Pre – Clinical malaria
Biotechnology
Univ . of Sao
Paulo
VLP VLPs SARS – CoV2 Pre – Clinical
peptides / whole
virus
50
In some embodiments , vaccines are all compounds as they are infectious , a superspreader is a person who may
disclosed in in the website of Clinical Trials.gov ( https : // excrete a normal ( or low ) number of pathogens during the
clinicaltrials [ dot ] gov / ct2 / results ? cond = COVID – 19 ) , which time they are infectious but this person is potentially and / or
are all incorporated herein by reference . Other vaccines may effectively in contact with a high number of people , there
be used as well . fore potentially infecting the same or more number of people
In some embodiments , treatment can be the use of as a person who excretes a higher than normal number of
Hydroxychloroquine and azithromycin plus zinc . 55 pathogens , as schematically shown for example in FIG . 1 .
In some embodiments , vaccines include the vaccine Following this logic , according to some embodiments of the
developed by the Moscow – based Gamaleya Institute , named invention , a superspreader is further identified according to
Sputnik – V . the number of people he / she can potentially be in contact
In some embodiments , providing a treatment as disclosed with , is expected or estimated to be in contact with ( e.g. ,
above to healthy subjects can be understood as prophylactic 60 based on number he has been in contact with ) , no matter the
treatment and / or vaccination . level of excretion of said person .
Exemplary Classification of Superspreader Super – Spreading Potential Score
Referring now to FIG . 1 , showing a schematic represen- In accordance with some embodiments of the invention ,
tation of a definition of superspreader , according to some there are provided methods and systems of providing sub
embodiments of the invention . In addition to the notion that 65 jects in a population with a “ superspreading score ” , which
a superspreader might be identified as a person who excretes will help to provide the order in which the subjects , option
a higher than normal number of pathogens during the time ally in groups of subjects , will receive treatments . In some
US 11,107,588 B2
49 50
embodiments , the higher the score the higher the potential of once the children are infected by the teacher , the children
each individual to spread the disease . In some embodiments , return home and potentially infect their families . While for
the higher the score , the earlier the individual should receive example , a doctor that works at a prison would potentially
the treatments . In some embodiments , a potential advantage receive a lower score since the incarcerated people in the
of vaccinating / treating individuals having the higher super- 5 prison are not leaving and probably will not infect anyone
spreading score is to block potential intersections where a higher number of individuals might be infected by the else ( the infection is contained to the prison alone ). potential superspreaders, and this is done by vaccinating Another example , if a certain subject meets only a certain individuals with potentially and / or actual higher chances to number of individuals, and mainly only those individuals, meet other people , and optionally also in relation to other 10 for example a subject in a close community, then that subject individuals ( for example by normalization of the data ). In will receive a lower score . some embodiments , a potential advantage of this method is Characteristics of Population that a Subject Actually Met that a population will potentially reach faster a state of herd In some embodiments, if a certain subject meets people
immunity , as the provision of treatments continues . that were identified as superspreaders , this will influence the
Referring now to FIG . 2 , showing a flowchart of an 15 score by increasing their score , also when compared to
exemplary embodiment of the invention . In some embodi subjects that do not meet superspreaders and / or regular
ments , the system and methods are based on the following : people . In some embodiments , the information regarding
receiving information about a subject 202 , analyzing the meeting a superspreader is performed between the mobile
received information 204 , generating a score 206 based on devices in an anonymous matter , for example , as will be
the information , optionally allocating the subject based to 20 further explained below .
the score to a score group 208 , and providing treatment The Nature of the Locations
according to the score and / or according to the score group In some embodiments , the nature of a location means if it
210. As will be shown below , some or all of the receiving is in a closed place , if it is in an open space , if it is indoors ,
and generating may be performed on an electronic device of if it is outdoors , quality of ventilation or any combination
subject 202 . 25 thereof. In some embodiments, the nature of the locations Exemplary Factors Influencing the Score can drastically change the score given to a subject . It has
In some embodiments , the score is generated utilizing one been shown that a likelihood of a subject transmitting a
or more factors and / or components, each influencing the pathogen increases by a factor of between about 10 times to final score by either adding or subtracting from the score . In about 100 times when the location is indoors and / or in a tshoem esc oermeb iond ia mleinnetasr , m tahtet eorn e( i onrc rmeaosrien g f/da cetcorresa scianng itnhfel suceonrcee 30 closed space . This is because the risk of infection is linearly, for example +1 to the score or -2 to the score ) increased due to the possible buildup of the airborne patho
and / or one or more factor can affect the score in a weighted gen – carrying droplets , the pathogen likely higher stability in
matter, as will be further explained below . Exemplary fac indoor air, and / or a larger density of people .
tors and / or components are one or more of the following : 35 In some embodiments , if the location is indoors or in a
Profession in Record of the Individual closed location , then the score given to the subject for a
In some embodiments , the profession of the individual is contact will increase .
correlated with a potential number of people the person In some embodiments , other factors that influence the
might be in contact with during a regular day of operation . increment or reduction of the likelihood of a subject trans
In some embodiments , individuals that potentially must 40 mitting a pathogen indoors are one or more of ventilation
meet many people due to their profession will receive a high rate , use of natural ventilation , avoidance of air recirculation
score . For example , cashiers at the supermarket , vendors in and use of air filters .
markets , bus drivers , delivery people , technicians , librarians , In some embodiments , the system will comprise infor
etc. In some embodiments of the invention , the profession mation on indoor locations related to the ventilation rate , use
information is used to estimate a contact quality score , for 45 of natural ventilation , avoidance of air recirculation and use
example , doctors being more careful with PPE than teachers . of air filters . In some embodiments , an indoor place com
It is a particular feature of some embodiments of the prising a high ventilation rate score will provide a lower
invention , that differences within such a group , such as score to the individual when compared to a place having a
between different doctors , are determined . In some embodi low ventilation rate score .
ments of the invention , a subject’s score is modified accord- 50 The Kind of Places Usually Visited by the Subject
ing to the profession , for example , to compensate for criti- In some embodiments , subjects that are prone to frequent
cality of the subject and / or to lack of control of the subject religious or secular events , like in a synagogue , a church or
( e.g. , a bus driver ) over number of contacts . a mosque or a dancing venue , where the people are in close
In some embodiments of the invention , a subject provides proximity to each other , and talk , pray , sing and / or breathe
profession information or other information used to adjust 55 deeply and / or mingle more , will receive a higher score ( e.g. ,
scoring by scanning a barcode ( or other machine – readable for such a contact event ) than those who do not frequent
item such as a barcode or RFID chip identity card ) which is religious events . In some embodiments , similarly to above ,
optionally digitally signed with such information . Option- also subject that are prone to frequent sports events will
ally , this allows the device to know the profession informa- receive a higher score . In some embodiments , places that are
tion , but may not allow the device and / or the information 60 frequented regularly by a large quantity of individuals
provider to link the request for data to a particular indi- ( including public transportation , detectable for example , by
vidual . Thus potentially maintaining privacy . geolocation and / or regular start – stop movement that matches
Characteristics of Population Potentially to Meet a public transportation profile and / or base don payment
In some embodiments , the kind of population that a activity using the tracking electronic device ) will be marked
certain subject can potentially meet will either increase or 65 as points on interest for the potential spreading of the
decrease the score . For example , teachers that meet many infectious disease / virus / pathogen , and subjects that frequent
children will be provided with a higher score , since if and those places will receive a higher score .
10
US 11,107,588 B2
51 52
The Length of Time at the Locations party informs that a person that showed low movement data
In some embodiments , the length that a subject stays in and received a low score is actually performing many
one place will contribute to the determination of the prob- movements , once the information is verified , the score will
ability to infect others and / or to be infected by others . For change accordingly . The contrary is also valid , for example ,
example , a subject that visits many places but stays there just 5 a third party informed that a person that showed high
for a minute or two might receive a lower score ( e.g. , for a movement data and received a high score is actually staying
contact event ) than a person that stays for longer in a few places , since staying longer at one place potentially at home , once the information is verified , the score may change accordingly .
increases the chances to infect and / or be infected . Dedicated Mandatory App Historical Geolocation Data of the Individual In some embodiments, in view of the pandemic, the In some embodiments , historical data of the location of an
individual is used to assess the potential geolocation activity government may order the citizens to install a dedicated
of that specific individual . For example , Google Maps® data application on their smartphones ( or other smart devices like
saved in servers , Waze data saved in servers , and other tablets , smart watches , smart glasses , etc. ) to help the
geolocation applications configured to save geolocation 15 government with the logistics of the vaccination procedures .
activity data . In some embodiments , individuals having a In some embodiments , the government ( or other body )
high volume of movement data ( and / or high usage of public provides the public with such dedicated smart devices . In
transportation ) in their historical geolocation data will some embodiments , the app and / or the smart device is
receive a high score . In some embodiments , the historical configured to inform on the user’s location at all times and
data is used to further assess a reliability of change in 20 to communicate with adjacent smart devices ( via Bluetooth
behavior of a subject , for example to determine if to increase for example ) to assess the interactions between users , for
score in cases where the actual geolocation data changes example vicinity between users , movement of users , etc. ) . In
drastically ( for example if there is a risk that a subject wants some embodiments of the invention , already existing soft
a higher score to receive the vaccine and increases his ware may be used , for example , both android and is based
movements to achieve so ) . 25 cellphones have software ( e.g. , as an operating system
Actual Geolocation Data of the Individual service ) which can detect proximity of others and such
In some embodiments , actual measured geolocation data software may be used or improved to provide functionality
of each individual is monitored to assess their potential to as described herein .
meet other people . In some embodiments , people which In some embodiments , such app can be used to provide
show high number movements during the day in areas where 30 information regarding how many unique people the user
other people are located will receive a high score . In some meets . For example , a certain user can meet many people but
embodiments , actual geolocation data of each individual is they are all the same people all the time . While another user
monitored using one or more of : can meet fewer people but each one is a different individual .
1. Electronic devices , for example the location provided by In some embodiments , the second user may potentially
the GPS of their own cellphones ; 35 receive a higher score and therefore receive treatment first .
2. Using face recognition technology based on one or more In some embodiments , such app and / or smart devices are
of : a ) video surveillance data received from available also used to assess the progression of the vaccination
sources , for example street cameras , ATM’s , private surveil- procedures and the efficacy of the vaccination procedure . In
lance cameras in stores , buildings and houses , etc .; b ) social some embodiments , individual data arriving from each user
media . 40 is coupled with their health information ( sick , vaccinated ,
3. Digital activity , for example credit card usage , IP address recovered , etc. ) to further assess the progression of the
used while using a computer or an electronic device , anten- vaccination procedures and the efficacy of the vaccination
nas that receive data while performing a phone call . procedure . Optionally , if the persons met by a user are
Optionally or additionally , such actual geolocation data is vaccinated or otherwise determined to be immune , such
used instead of or in addition to actually identifying contact 45 contacts may not count and / or be weighted lower .
between people . In some embodiments , the app will be also used to send
Historical Medical Data of the Individual personalized communication to the users , for example , to
In some embodiments , historical medical data of each come and be vaccinated . In some embodiments , in view of
individual is assessed to provide a score . For example , as the information received from the app , specific actions are
mentioned above , individuals with chronic coughing will 50 taken , for example , send a communication to the user to
receive a high score since they have potentially a higher enhance his awareness to behavioral rules during pandemic ,
chance to transmit the infectious disease / virus / pathogen . In to come and be vaccinated , to avoid certain locations , which
some embodiments , individuals having a background con- are at high risk of contagion .
dition that enhances the chances of transmitting the disease Dedicated Voluntary App
will receive a high score . In some embodiments , in view of the pandemic , the
Actual Medical Data of the Individual population is encouraged to install a dedicated app , where
In some embodiments , during the pandemic , every new those that do install the app are rewarded . In some embodi
medical data concerning each individual is monitored to ments , the reward is priority to receive treatment .
assess if the new data indicates a change in the medical Monitoring Behavior of Subject
status of the individual regarding their potential to infect 60 In some embodiments , the behavior of the subject is
others . Using the example above , if a person is diagnosed monitored in relation to safety features performed by the
with chronic coughing it will increase their score ( e.g. , in subject , for example , wearing a mask ( e.g. , analyzing
general and / or per contact ) . images taken during calls or other looking at screen of
Third Party Information Regarding the Individual cellphone ) , washing his hands ( e.g. , analyzing sounds of
In some embodiments , third party information from indi- 65 water running or movement by a smartwatch ) , keeping
viduals informing on others will be assessed to decide if the social distancing ( e.g. , based on Bluetooth power levels
information needs to affect the score . For example , if a third and / or NFC detection ) , moving between multiple locations ,
55
US 11,107,588 B2
53 54
etc. In some embodiments , these are monitored using the 3. The time length of the potential and / or actual encounter
same devices / methods as disclosed above . of the subject with the other subjects .
Exemplary Scoring Method In some embodiments of the invention , the score is
In some embodiments , each individual in a population updated for and / or after each contact event . In some embodi
( e.g. , above 100 , 1000 , 10000 and / or 100000 individuals ) is 5 ments of the invention , update is at end of the day , which
provided with a score defining the potential level of super- may allow aggregating multiple meetings with a same
spreading of each individual . In some embodiments , scores person . Optionally or additionally , the score is updated per
are defined as number of contacts ( see herein ) , and the a set of contact events . In some embodiments of the inven
number of contacts that are counted are from about 10 to tion , the score is calculated after all contact events are
about 100 , optionally from about 100 to about 1000 , option- 10 collected , for example , based on an analysis of a contact
ally from about 1000 to about 10000 , for example 100 , 400 , network to identify individuals , which , if vaccinated , will
1000 , 2000 , 10000 or intermediate or greater numbers . In best stop infection . Such analysis may be carried out by
some embodiments , a high score defines a high potential of simulating the contact network and trying out various vac
superspreading , while a low score defines a low potential of cination schemes and / or removal of various individuals
superspreading . In order to facilitate the explanations of the 15 and / or sets of individuals . invention , a scoring scale from 0 to 100 will be used . It From Score to Treatment
should be understood that other scales can be used , like In some embodiments , once the scoring of each individual
heat – map scoring , decimal order scales , etc. , all of which are is achieved , or optionally the scoring of a high number of
included in the scope of the invention . In some embodiments individuals of the population , a list is created having the
of the invention , the score is open ended . In some embodi- 20 order in which each individual will receive the treatment . In
ments of the invention , the score is normalized , for example , some embodiments , the list is optionally divided by groups ,
to other scores . The normalization need not be linear . In for example , all the individuals that scored between 100 and
some embodiments of the invention , the score is a scalar . In 90 are grouped in group A , which will receive first the
some embodiments of the invention , the score is multi- treatments . Then all the individuals that scored between 90
dimensional , for example , including a superspreader poten- 25 and 80 are grouped in group B , which will receive second
tial dimension and a variability in behavior dimension ) the treatments , and so on .
In some embodiments , the score is calculated using Informing the Public
weighted scoring models , in which one or more factors In some embodiments , once the list is made , individuals
and / or components are assessed according to the received will be informed on when and where to go and receive the
information data . Referring now to FIG . 3 , showing a 30 treatments , for example , by means of emails , dedicated apps
schematic flowchart of a method of calculating a weighted in their cellphones , over the media , etc.
score , according to some embodiments of the invention . In Exemplary Simulations
some embodiments , the system receives information data In some embodiments , models and simulations are run in
about a subject 302. In some embodiments , the information dedicated computers , for example , to assess the potential
data is divided according to the source of the information 35 progression of the treatments and the probable time to reach
data 304 , for example , electronic information 306 from herd immunity and / or select values for various parameters .
smartphones , cameras , credit card information , etc. , geo- In some embodiments , simulations include the insertion of
graphical information 308 , for example from GPS or cell one or more of actual data received from individuals ,
towers , governmental information 310 , for example from the simulated data of / from individuals ( in case is necessary to
census bureau or EMR ( electronic medical records ) , human 40 run probable scenarios ) . In some embodiments , evaluations
information 312 , for example from other individuals calling and models utilize one or more of neural networks , machine
an providing the information about other individuals , and learning and dedicated simulations .
one or more of the factors and / or components disclosed In some embodiments , the simulations take under con
above . In some embodiments , the system then calculates a sideration and model the probability of the treatments to
weighted score of each information , optionally according to 45 work ( or not work ) on the individual .
a predetermined criterion 314. In some embodiments , the In some embodiments , the simulations take under con
system then generates a total score from the different sideration and model the kind of population that a certain
weighted scores , optionally according to a predetermined subject can potentially meet and the potential population
criterion 316. In some embodiments , the system then pro- those individuals will potentially meet afterwards . For
vides a list comprising an order of treatment , which is then 50 example , teachers that meet many children will be provided
used to actually treat the population 318 . with a higher simulated score , since if and once the children
In some embodiments , the score comprises a plurality of are infected by the teacher , the children return home and
components , for example predicted likelihood of a subject potentially infect their families . While for example , a doctor
transmitting an infectious disease / virus / pathogen , predicted that works at a prison would potentially receive a lower
likelihood of a subject contracting an infectious disease / 55 simulated score since the incarcerated people in the prison
virus / pathogen , relative health risk to a subject if said are not leaving and probably will not infect anyone else ( the
subject contracts a infectious disease / virus / pathogen , dam- infection is contained to the prison alone ) .
age to society if the subject contracts a infectious disease / In some embodiments , simulations are performed to
virus / pathogen ; one or more of the above optionally in view evaluate parameter values used to identify a superspreader
of physical proximity data to other subjects . 60 and possibly how to differentiate them from regular indi
In some embodiments , physical proximity data of a sub- viduals .
ject with other subjects is calculated by including one or Exemplary Spreading Network
more of : In some embodiments , before , during and / or receiving the
1. The number of subjects the subject potentially is in information regarding the individuals in the whole popula
contact with ; 65 tion , a network 400 of the population is created , as shown for
2. The potential and / or actual distance of the subject to the example in FIG . 4. In some embodiments , the network is
other subjects ; constantly updated by the system . In some embodiments , the
9 US 11,107,588 B2
55 56
network is used to determine the potential spreading of the Exemplary Privacy Settings
infectious disease / virus / pathogen if a certain individual is In some embodiments , the system comprises one or more
infected . In some embodiments , when possible , clusters in layers of protection and / or privacy . In some embodiments ,
the network are identified , for example clusters 402 through layers of protection include one or more of encryption
412 in network 400. In some embodiments , when evaluating 5 algorithms and / or software .
whom to provide treatments , the system assesses the indi For example , encryption algorithms and / or software con
viduals in the clusters and performs analysis and simulations vert the data into ciphertext to transform the original data to
to choose which individuals to treat ( e.g. , individuals that a non – readable format accessible only to authorized parties
interconnect clusters ) . In some embodiments , this is per who can decrypt the data back to a readable format . The
formed in addition to the scoring performed and generated 10 process of encrypting and decrypting messages optionally on each single individual . For example , it can be seen that involves keys. The two main types of keys in cryptographic individual 414 belongs to both clusters 402 and 404 , thereby systems are symmetric -k ey and public – key ( also known as creating a potential bottleneck ( or bridge ) between clusters . asymmetric -k ey ). Exemplary types of keys : Symmetric – keys : In symmetric
Therefore , it would be advantageous to treat individual 414 15 key schemes , the encryption and decryption keys are the to protect cluster 404 from potential infections coming from same . Communicating parties must have the same key in
cluster 402. Same logic is applied to individual 416. Treating order to achieve secure communication . Public Keys : In
individual 416 can potentially protect clusters 410 and 412 public – key encryption schemes , the encryption key is pub
from potential infections coming from cluster 402. In some lished for anyone to use and encrypt messages . However ,
embodiments , the system identifies potential key individuals 20 only the receiving party has access to the decryption key that
and / or potential key groups of individuals to treat first in enables messages to be read . In some embodiments , the
order to potentially protect clusters of individuals . In some length of the encryption key used in the system is one or
embodiments , the systems performs this assessment in view more of 128 – bits , 256 – bits , 1024 – bits and 2048 – bits .
of the number of doses available to the population . For In some embodiments , the privacy of the users that
example , if there is a large number of doses , instead of 25 information is being collected is protected by anonymizing
treating the individuals located in the bottlenecks , it might the user at the source . For example , when a cellular phone /
be advantageous to treat first all individuals in the cluster electronic device is used to collect the relevant data , the
402 , thereby potentially protecting the rest of the clusters name of the owner of the electronic device is either
from infection . encrypted and / or anonymized so any interaction with exter
In one example , the system selectively removes individu 30 nal sources ( for example the servers of the systems ) will be als to identify which set of N individuals ( e.g. , where N is managed without the use of the actual name of the user but using an encrypted and / or anonymized user name . In a
the number of doses to be used ) is best to remove . This can practical example , electronic devices / cellphones are used
be done using brute force approaches , e.g. , of trying a evaluate , quantify and qualify the interactions of the user
plurality of sets . Optionally or additionally , this is done by 35 with other people during the day . In some embodiments , the
selecting sets of individuals ( e.g. , based on some shared cellphone communicates with other cellphones to monitor
characteristic , such as profession or place in the network ) the interactions ( distance , location , duration , etc. ) . In some
and seeing the effect of vaccinating these individuals . embodiments , when collecting the data about the interac
Optionally or additionally , a different search technique is tions , the software in the electronic device will use
used , e.g. , treating the problem as an optimization problem . 40 encrypted and / or anonymized user names . For example ,
Exemplary Use of the System and Methods for Testing using the names as mentioned in the example below , John
Doe , Jane smith and Mark Lite are three users , all having
In some embodiments , the system and methods are used cellphones and optionally comprising a dedicated app for
to identify selected subjects to be tested for the disease . In this purpose . In some embodiments , the software of the app
some embodiments , the testing is used to assess one or more 45 in the electronic device will encrypt and / or anonymize the
of the progress of the disease , the progress of the treatments , to be , for example , John Doe = user
the progress of the herd immunity , etc. 265498756124565526 , Jane smith = user 31678465923128
Exemplary Use of the System and Methods for Determining and Mark Lite = user 463212887036554. From this point on ,
Who Will Receive a Certain Type of Vaccination all communications between their electronic devices and
50 external sources will be performed using the encrypted In some embodiments , during the development of vac and / or anonymized user names . Optionally , for example as
cines for a certain disease , different vaccines comprising described below , the user IDs or what is exchanged between
different vaccine potencies are developed . In some embodi- telephones ) are non – unique . For example , provided at a ratio
ments , vaccine potency is a quantitative measure of the of , for example 1 : 100 , 1 : 1000 , 1 : 10000 , 1 : 100000 between
specific ability of the vaccine product to achieve an intended 55 codes and individuals . While this may mean a potential for
biological effect defined in a suitable biological assay based confusion between individuals , such confusion may be
on the attribute of the product that is linked to the relevant small , while the increase in difficulty of identifying a use
biological properties . In some embodiments , the system is based on the tracked information can significantly increase .
used to identify which individuals will receive which types Furthermore , when assessing the order of receiving treat
of vaccines in relation to their potency . For example , indi- 60 ment , either individually or by groups , ( e.g. , at a server ) may
viduals that received and / or were identified as a high super- comprise the parameters needed to enter a certain group ( for
spreading score by the system would be vaccinated with example , the first group to receive treatment , the second
more potent vaccines , when compared with other individu- group to receive treatment , etc. ) . In some embodiments , the
als having lower superspreading scores . In some embodi- action of comparing between the parameters of each group
ments , those individuals having lower superspreading scores 65 and the collected data from the user will be performed inside
might either receive later a vaccination or receive a vaccine and by the electronic device itself , thereby avoiding sending
having a lower potency . data to the servers . In some embodiments , the electronic
names
9 US 11,107,588 B2
57 58
device will contact the server to requests the parameters , the 2. A system that uses personal information but does not
electronic device will perform the necessary calculations transmits specific information that could be used to poten
and will generate a score that will be sent back to the server tially identify the individual ; and
in an anonymized matter ( as explained before ) . In some 3. A system that does not require any personal information
embodiments , additional information regarding each indi- 5 to work .
vidual user , as disclosed above , is also downloaded to the In some embodiments , the anonymization techniques
electronic device for use of calculations . Once the calcula- described in the “ Exemplary Privacy Settings ” section
tions are finished , the resulting data will be sent to the belong to the first type and / or the second type of technique ,
servers and , in response , the server will optionally send a where relevant data ( positional data , personal data , etc. ) is
notification to the user to go and receive treatment . 10 used by the system but : anything that is transmitted is
It is a particular feature of some embodiments of the either coded and / or anonymized when used , or b ) the
invention that information about a person’s activities , loca- necessary calculations are performed on the electronic
tions , meetings , are not sent out of the device except as , for device itself , thereby avoiding sending any personal data at
example , an overall score or a priority for treatment . In some all .
cases , the behavior is sent out but is anonymized and / or 15 In the following paragraphs , systems belonging to the
condensed , for example , indicating a number ( e.g. , option- third type of system comprising a method of identifying a
ally not an exact number and / or time and / or date ) of people superspreader that potentially does not require the use of any
met and a number of large congregations attended ( option- personal information will be explained .
ally not an exact number and / or location ) , but with enough Exemplary “ ID ” Based System for the Identification of
details removed so that identification of an identity of the 20 Superspreaders
device owner will be difficult or impossible . In some embodiments , the system is based on the follow
In some embodiments , whether the calculations are per- ing assumptions : 1 ) all individuals comprise an electronic
formed on the servers or on the electronic device , the device of any kind ; 2 ) on each electronic device there is
encryption and / or anonymizing of the name of the user is installed a dedicated application / app that runs the system’s
always used . In some embodiments , the means to read 25 software ( as will be explained in the following paragraphs ) ;
between the encrypted / anonymized user name and the actual and 3 ) when individuals meet other individuals , information
name will only be available in the user’s electronic device . is passed between their electronic devices .
In some embodiments , the notification for getting treat- Referring to FIGS . 5a – f , showing flowcharts of exemplary
ment may or may not contain information regarding the methods of identification of superspreaders , with an anony
results of the calculations . For example , an individual that 30 mization , according to some embodiments of the invention .
was identified as a superspreader may or may not receive In some embodiments , the method begins when a user
information about the fact that he / she was identified as such . downloads the software , in the form of an application ( or
In some embodiments , the potential advantage of not pro- app ) into their electronic device 502. In some embodime ats ,
viding such information is to further enhance the privacy dedicated electronic devices comprising the software will be
protection of the user . For example , an onlooker may not be 35 distributed to those individuals who either do not possess an
able to tell if a user received a high score due to his own electronic device or do not want the software downloaded
behavior , the behavior of those he meets and / or an under- into their electronic devices . In some cases , the device has
lying health condition , which may put them at higher risk . such software preinstalled thereon .
In some embodiments , dedicated codes , for example in In some embodiments , when the individual opens the
the form of coupons , will be provided to individuals having 40 application , optionally , the individual will be requested to
important / relevant professions ( like doctors , police , etc. ) . In provide and / or insert an identification ( ID ) , optionally using
some embodiments , insertion of the codes into their personal alphanumeric digits 504 , optionally comprising a high num
electronic devices will inform the system that that ber of digits , for example 10 digits , 20 digits , 40 digits . In
encrypted / anonymized user needs a correction in their score . some embodiments , the system will automatically provide
In some embodiments , the correction can be either increas- 45 an ID to the device ( e.g. , will be generated locally , for
ing the score or decreasing the score . In some embodiments , example , as a random number or as an encrypted version of
when the electronic device detects certain behavior , like an the user ID . To facilitate the explanations below , a 20 digits
increase in the movements of the user , the electronic device ID will be assumed . It should be understood that other length
( for example via the dedicated app ) will warn the user that of ID can be used , noting the difference between IDs that are
his score will be changed if the behavior is not changed . In 50 expected unique and IDs that are not expected to be unique
some embodiments , changing the score can be either and within unique IDs , IDs that also a particular part thereof
increasing or decreasing the score . is long enough to be expected to be unique .
Exemplary Methods for Identifying Superspreaders with At this point , all users have an electronic device with a
High Levels of Anonymization software in the form and / or as part of an application in which
It has been shown that individuals are concerned that the 55 an ID comprising 20 digits has been assigned to the device .
authorities and / or companies are constantly collecting data It should be noted that the use of “ application ” , “ app ” and
with or without their consent for a plurality of reasons . It is “ software ” are interchangeable for the explanation of the
also scope of some embodiments of the invention to provide following methods . From here , four different methods can
a method of identifying superspreaders without the need to be used , as will be further explained bellow .
collect data that could potentially be used to lead to the 60 Anonymized Method 1 – Count
identification of the person in question . Referring to FIG . 5b , showing a flowchart of exemplary
As an example , consider three types of systems having anonymized method 1 , according to some embodiments of
different levels of possible anonymization techniques , in the invention . Following the letter “ A ” from FIG . 5a to FIG .
accordance with various exemplary embodiments of the 5b , in some embodiments , when an electronic device comes
invention : 65 in proximity to another electronic device , the devices
1. A system that uses personal information but does not exchange full IDs 506 between each other , and the software
transmits that personal information about the individual ; saves the received ID in the application itself . In some
US 11,107,588 B2
59 60
embodiments , after a certain period of time , for example , In some embodiments , the user choses the level of ano
after one day , after 7 days , after 14 days , after 30 days , or nymity that the system will work ( completely anonymous or
intermediate or shorter times and / or on request by a central partially anonymous ) , e.g. , different individuals may have
server , the application analyzes the IDs stored in the elec- different anonymity levels in a same vaccination prioritiza
tronic device 508. In some embodiments , analyzing com- 5 tion system .
prises one or more of counting the number of IDs that were Anonymized Method 2 – Count with Transmission of Partial
received , the number of times that a specific ID was received Username
and the number of IDs received in a day . In some embodi- Referring to FIG . 5c , showing a flowchart of exemplary
ments of the invention , the counting is weighted so different anonymized method 2 , according to some embodiments of
IDs get a different weight , for example , IDs with a high 10 the invention . Following the letter “ B ” from FIG . 5a to FIG .
score may be weighted higher , for example as described 5c , in some embodiments , when an electronic device comes
herein . In particular , IDs that are associated with contacting in proximity to another electronic device , the devices
other suspected superspreaders may receive a higher score . exchange partial IDs , for example only the last 10 digits of
In some embodiments , the software then generates a score the 20 digits of the ID 524 , and the application saves the
based on the result of the analysis . 15 received partial ID in the application itself . In some embodi
At this point one of two different methods is optionally ments , the partial ID is a substantially unique partial ID . For
applied , a completely anonymous method and a semi – anony- example , the use of the last 10 digits of the 20 digits
mous method 510 . increases the chances that the partial ID is a substantially
In some embodiments , when the method is a completely unique partial ID . In some embodiments , the partial ID is a
anonymous method , the method continues following the 20 substantially non – unique partial ID . For example , the use of
letter ” E ” back to FIG . 5a . the last 3 digits of the 20 digits increases the chances that the
In some embodiments , the application receives from the partial ID is a substantially non – unique partial ID , since
server a scale of scores 512. For example , continuing using there is an increased chance that the same last 3 digits appear
the scale as above , from 1 to 100 , group 1 are those in more than one ID . It should be understood that the word
individuals having a score higher than 90 , group 2 are those 25 “ substantially ” in this context does not mean to be vague ,
individuals having a score from 80 to 90 , and so on . In some but it is related to the statistical probabilities that a presented
embodiments , the software then compares the score gener- partial ID could be identical to another .
ated from the analysis with the scale of scores 514. In some In some embodiments , a potential advantage of exchang
embodiments , based on the result of the comparison , the ing only partial IDs is that it decreases the chances that the
software provides the user of the device with relevant 30 specific individual could be identified . It is also noted that ,
information related the treatment to be received . For in some embodiments , transmitting partial ID might intro
example , a predetermined date to receive vaccination ( infor- duce errors to the analysis of the meeting between individu
mation received with the scale of scores from the server ) als since it increases the possibility that one or more indi
and / or the group number for receiving the vaccination . In viduals will transmit the same partial ID . Since the scope of
some embodiments of the invention , the scale of scores is 35 the method is to protect the privacy of the individuals while
generated by the receiving information about the score contemporarily providing an indication of a potential super
distribution and selecting cutoff values optionally based on spreader , a certain margin of error is acceptable .
available vaccines . Optionally , the information comprises In some embodiments , when a received partial ID is
receiving scores form some or all devices . Optionally , only stored in the application , it is stored ( or only transmitted that
a statistical same of scores is used , for example , fewer than 40 way ) by adding its own partial ID . In some embodiments , a
10 % , 1 % , 0.1 % of available devices , for example , between potential advantage of using this method is that if such pairs
50 and 10,000 scores . It is noted that such scores may be of partial ids are transmitted to a third party , such third party
delivered anonymously , for example , using an anonymous can track and count unique meetings .
web service , optionally anonymized using anonymity tools In some embodiments , after a certain period of time , for
such as Tor , so that the deliverer of each score is unknown . 45 example , after 7 days , after 14 days , after 30 days ( or other
Optionally , the scores are digitally signed by the sender . times for as discussed in the previous method ) , the appli
Returning to FIG . 5b , in some embodiments , when the cation analyzes the partial IDs stored in the electronic device
method is not a completely anonymous method , the method 524. In some embodiments , analyzing comprises one or
continues following the letter “ F ” to FIG . 5f . more of counting the number of partial IDs that were
In some embodiments , after the software has generated a 50 received , the number of times that a specific partial ID was
score based on the analysis , the software sends the score , received and the number of partial IDs received in a day . In
together with the full ID ( here and in other examples , a full some embodiments , the software then generates a score
ID may be encrypted or Hashed or otherwise used to based on the result of the analysis . In some embodiments of
generate a token , which , optionally , is not decipherable by the invention , a repeat meeting with a same partial ID is not
the server ) , to the server to be used to evaluate if that specific 55 counted or given a lower weight . Other methods of counting
individual is potentially a superspreader or not , when com- as described herein may be used . In some embodiments of
pared to other users 518. In some embodiments , the server the invention , the count is otherwise normalized . For
performs an evaluation by comparing the scores of the example , the distribution of counts may be used to recon
different IDs 520 and generates a treatment list according to struct an estimate of actual diversity of meetings , using
the result of the evaluation . In some embodiments , the server 60 statistical methods of distribution estimation , such as known
then sends back notification regarding the vaccination pro- in the art . Such methods may also be used if instead of
cedures 522 , for example , when to go to receive a vaccina- always transmitting the ID the ID is only sometimes trans
tion , the group number , etc. mitted . This statistical distribution may be used to estimate
In some embodiments , optionally , the user can choose to the percentage of unique meetings vs percentage of repeat
respond to a series of personal questions presented by the 65 meetings , for example , assuming a given distribution shape
application , which are then translated into factors that affect for repeat meetings . Such a given shape may be provided ,
the score , for example as disclosed herein . for example , by a central server ( e.g. , based on real – time data
US 11,107,588 B2
61 62
collection ) or a priori . Optionally or additionally , such the first number is as disclosed in method 1 , where the full
distribution may be created by sometimes applying method ID is exchanged . In some embodiments , the exchange of the
1 of full ID transmission . first number is as disclosed in either method 2 or method 3 ,
At this point , one of two different methods is optionally where a partial ID is exchanged . For the explanation of the
applied , a completely anonymous method and a semi – anony- 5 method and as disclosed in FIG . 5e , the explanation will
mous method 528. In some embodiments , when the method refer to the transmission of a partial ID . It should be
is a completely anonymous method , the method continues understood that this method could also be applied when
following the letter “ E ” back to FIG . 5a . transmitting the full ID .
In some embodiments , when the method is not a com- In some embodiments , the first number is used to evaluate
pletely anonymous method , the method continues following 10 the number of contacts .
the letter “ F ” to FIG . 5f . These alternatives may be applied In some embodiments , the second number to be
as above . exchanged is a different set of digits , either created by the
Anonymized Method 3 — Count with Transmission of Partial system or inserted by the user itself 538. In some embodi
Username and Username Changes Periodically ments , the actual second number to be exchanged is a partial
In this method , which can be used as a variant of the last 15 second number , similar to what is done with the first number .
two methods , and is shown in FIG . 5d , the ID or partial ID In some embodiments , the second number is used to
used by the device is modified . evaluate if the individual is meeting people from outside a
In some embodiments , for example , after the certain limited subpopulation and / or track the general promiscuous
period of time mentioned above for counting , the partial ID ness ( optionally in a non – sexual sense ) of such individuals .
that is used for the transmission of IDs between is changed 20 In some embodiments , contrary to the first number that
by the system and / or the individual itself 534. The actual ID always is exchanged in an encounter , the second number is
may be changed or a different part of the ID transmitted . In exchanged at a certain “ rate of probability ” . In some
some embodiments of the invention , the original ID is used embodiments , a rate of probability is , for example , a calcu
as a seed to generate a series of pseudo random IDs to be lated number that responds to the question : what is the
used for transmission . In some embodiments , for example , 25 percentage rate necessary to separate between a super
when the system changes the transmitted partial ID , the spreader and a non – superspreader . In some embodiments ,
system transmits instead of the last 10 digits of the ID , the the rate of probability is achieved by running a simulation ,
first 10 digits of the ID ; or for example the first 5 digits and checking for different probability rates the degree of
together with the last 5 digits . It should be understood that discrimination . For example , a rate of probability can be 3 % ,
the above – mentioned are only examples , and that other 30 5 % , 10 % , 20 % or smaller or intermediate values . In some
methods of randomizing the partial ID that is transmitted are embodiments , this means that , if the rate of probability is 3 %
also included in the scope of some embodiments of the for example , an electronic device that encounters 100 elec
invention . In some embodiments , periodically changing the tronic devices will exchange 100 times ( 100 % of the times )
partial ID may further cause to errors since it further the first number and 3 times ( 3 % of the times ) , in addition
increases the possibility that one or more individuals will 35 to the first number , will also exchange the second number .
transmit the same partial ID . As mentioned above , a further In some embodiments , the rate of probability is lower than
certain margin of error is still acceptable . 100 .
The method then continues with various options for acting In some embodiments , from the moment the system is
on the score , for example , a completely anonymous method activated , the electronic devices of the individuals will begin
and a semi – anonymous method 536. In some embodiments , 40 collecting first and second numbers as long as they continue
when the method is a completely anonymous method , the to meet other electronic devices .
method continues following the letter “ E ” back to FIG . 5a . In some embodiments , when a certain electronic device
In some embodiments , when the method is not a com- exchanges the second number ( under the rate of probability ) ,
pletely anonymous method , the method continues following the electronic device will exchange in addition to its second
the letter “ F ” to FIG . 5f . 45 number , all second numbers that were collected until that
In this and other embodiments it is noted that other follow moment . In some embodiments , potentially and probabilis
up activities may be provided in addition or instead , in tically , an individual that is a superspreader will collect a
particular , activity by a central server may be reduced . For high number of second numbers because he / she meets many
example , a user may simply go to a vaccinating station and different individuals , who themselves meet different indi
show their score and be given a vaccination or date therefore 50 viduals . While an individual “ trapped ” in a subpopulation
accordingly . may only collect at most as many numbers are there are
Anonymized Method 4Complex Count with Transmission persons in the subpopulation . Therefore , in some embodi
of Partial Username , at Least One Additional Number and ments , when someone meets that superspreader , many sec
Optionally Username Changes Periodically ond numbers will be potentially exchanged from that super
Referring to FIG . 5e , showing a flowchart of exemplary 55 spreader to that someone . In some embodiments , those
anonymized method 4 , according to some embodiments of second numbers collected from other individuals will later
the invention . In some embodiments , a complex count be used to indicate a specific meeting between an individual
method is used for probabilistically determining if a certain and a superspreader .
individual is a potential superspreader . In some embodi- In some embodiments , an individual that collects many
ments , the complex count method comprises the use of two 60 second numbers , potentially and probabilistically , met a
independent counts for the determination . superspreader and / or is one themselves . In some embodi
Following the letter “ D ” from FIG . 5a to FIG . 5e , in some ments , this information is used to cause an effect ( e.g. ,
embodiments , when an electronic device is in proximity to increase ) in the scoring of the individual and / or in the weight
another electronic device , the system is configured to of the contact .
exchange not one , but at least two ID numbers as following . 65 The collected IDs may be counted after a time , e.g. , as
In some embodiments , the first number to be exchanged described in the other methods ( 540 ) In some embodiments ,
is the partial ID 538. In some embodiments , the exchange of optionally , after the certain period of time mentioned above ,
9
10
US 11,107,588 B2
63 64
the partial ID transmitted between devices is changed by the Such tracking can be by time and / or can be by change in
system and / or the individual itself 542 as disclosed above . count of first and / or second IDs that an individual has , which
Optionally , a method of follow – up is selected , for count ( and / or a date of contact ) is optionally transmitted
example , a completely anonymous method and a semi upon meeting and may be stored .
anonymous method 544. In some embodiments , when the 5 Exemplary Effect of Meeting an Individual that has Met
method is a completely anonymous method , the method Potential Superspreaders
continues following the letter “ E ” back to FIG . 5a . Referring now to FIG . 6 , showing a schematic flowchart In some embodiments , when the method is not a com pletely anonymous method , the method continues following of an example of the effect caused when a certain individual meets another individual that had been in contact with the letter “ F ” to FIG . 5f, for example as described above . possible superspreaders, according to some embodiments of In any of the above methods , optionally , statistical infor
mation about collected first and / or second numbers ( e.g. , the invention . In some embodiments , as previously men
how many people had how many collected first and / or tioned , when a Device A meets Device B 602 , IDs are
second numbers ) may be transmitted to the server to help exchanged and optionally also information regarding previ
generate a better picture of these statistics of the popula- 15 ous meetings 604. In some embodiments , for example , the
tion’s collected information . software in Device A , that has just received the ID and
In some embodiments of the invention , more than one previous meetings of Device B , will evaluate the received
second number is used . Optionally , each additional such data 606. In some embodiments , evaluation of data com
number is transmitted at a different probability . This allows prises one or more of evaluating the number of meetings
different numbers to give information about different char- 20 Device B has had 608 and the kind of individuals were met
acteristics of subpopulations . It is noted that if only one during those meetings 610. In some embodiments , since
number is used and its transmission rate not selected cor- these operations were also previously performed by Device
rectly , it may result is propagation of such second number B during its meetings , the information about the possible
over a significant part of the network of contacts , making it meeting with a potential superspreader will be also delivered
less useful for identifying more closed and more open parts 25 by Device B to Device A , when information is exchanged .
of the network . In some embodiments , the software in Device A will gen
In some embodiments of the invention , no additional erate a score to the meeting between Device A and Device
second number is used . Rather the first number is optionally B , also in view of the information regarding the kind of
counted and / or transmitted using such probabilistic trans- individuals that Device B has met 612. In some embodi
mission rate . So , for example , during a contact , the second 30 ments , the score is then saved in Device A 614 to be used in
device will store the received ID of the first contact in a the final score calculations , as previously described .
memory for storing and / or counting contacts with a first ID Exemplary Methods
and also , with some probability store that number in a In some embodi its , an exemplary method of providing
second memory used for counting and / or tracking second the order of treatments to a population comprises :
numbers . Additional memories may be provided if more 35 1. Collecting relevant data regarding each individual in the
numbers are tracked . population , according to predetermined parameters .
In some embodiments of the invention , a relatively small 2. Providing a superspreading score to each of the individu
non – unique ID is used and this ID may be used as an index als according to a formula using the predetermined param
for the first and / or second memory . For example , when eters .
meeting an individual who passes a non – unique ID 234 , 40 3. Ordering each individual according to his or her super
memory location 234 is increment ( optionally in a weighted spreading score from high to low .
manner ) . If a second ID list , say ( 123 , 456 , 789 ) is passed , 4. Optionally dividing all individuals in groups according to
the count in each of those indexes in the second memory is their superspreading score .
incremented ( optionally in a weighted manner ) . In some In some embodiments , after the list is ready , optionally in
embodiments , only one bit ( or an equivalent thereof ) is 45 groups :
saved for each ID in the second memory and it is either set 5. Notifying the individuals with a location and a time to
or unset . Optionally , the second ID uses more bits than the receive the treatments .
first ID , for example , 2 , 3 , 4 , 5 times as many bits or an 6. Treating the population according to their superspreading
intermediate of smaller or greater number . This may allow score , optionally by groups , where individuals and / or groups
preventing saturation of second ID tracking . Optionally or 50 hiving the higher scores will receive first the treatments . In
additionally , a statistical estimation of the actual number of some embodiments of the invention , treatment is rather
second IDs is reconstructed using statistical methods and the testing , as testing superspreaders may be a faster and more
number of second IDs received and optionally a count of at effective way of detecting a resurging pandemic .
least a sample thereof . Optionally , an assumption is made Exemplary System
about the expected shape of distribution of second IDs . In some embodiments , the system comprises a computer
Optionally or additionally , the number of second IDs network architecture optionally with machine learning and /
collected is tracked as a function of time . Optionally , poten- or other artificial intelligence tools to allow for the auto
tial superspreaders ( and which get an increased score and / or mated prioritization of treatments in a pandemic event . In
contact weight ) are those who early on accumulate a larger some embodiments , the system allows for prioritization of
number of second IDs ( e.g. , as compared to other persons an 60 treatments using information regarding subjects in a popu
individual comes in contact with ) and / or those persons ( e.g. , lation , disease process and progression , number of available
with repeated contact ) whose second ID count asymptotes treatment doses , and a plurality physical location attributes .
later or not at all . In some embodiments , this potentially enables relevant
Regarding repeat meetings with an individual , it is noted authorities to measure , predict and / or improve their health
that an individual is a sum of all his contacts , so that after 65 related performance during a pandemic . In some embodi
a time , if and as that individual meets new contacts , the ments , this in turn enables relevant decision – making person
individual changes and should be weighted more heavily . nel and healthcare providers to get a true quantitative sense
55
US 11,107,588 B2
65 66
of what is possible to achieve with any given population of then predict the performance of an underperforming vacci
patients , in view of the parameters that define each indi- nation result ( if no changes are made to trend performance )
vidual and the population . and predict the performance of the same treatment result if
The following is an example of the workflow of a user the requirements are met , and then compare the before and
experience with a system of the present invention : 5 after predicted performance to show the impact of meeting
1. A user makes a request for an analysis and list generation the requirements . A report of the requirements and of the
report to the system . predicted impacts of meeting the requirements may then be
2. The system uses an analytics module ( A.M. ) to analyze prepared by the system , and transmitted to the user .
the information of the population ( for example , information FIG . 7 schematically illustrates components of an exem
as disclosed above ) . 10 plary system comprising a computer network architecture
3. The system automatically issues a request to a Database usable in some embodiments of the invention , comprising at
Module ( DB.M ) to provide all relevant information and / or least one optional server 702 , an optional analytics module
issues a request to external sources ( see above ) to provide ( A.M. ) 704 , an optional Database Module ( DB.M ) 706 ,
the required information and / or issues a request to a simu- and / or optional access to various third – party databases and
lations module ( S.M ) to perform the necessary simulations . 15 sources 708 , and an optional simulations module 712 .
4. The analytics module ( A.M. ) collates the results into a In some embodiments , a user using a user device 710
unified analysis response , based on any combination of the accesses the at least one server 702. In some embodiments ,
subjects in the population and factors and / or components the user transmits a user request to the analytics module
data available . In some embodiments of the invention , the ( A.M. ) 704 for analysis of data and the generation of a list
A.M includes a ML module ( optionally in the form of an 20 716. In some embodiments , analytics module ( A.M. ) 704
analytic system or a neural network ) which is used to predict accesses the Database Module ( DB.M ) 706 either directly
transmission and super – spreader potential of an individual and / or via the server 702. In some embodiments , the ana
based on their past behavior . Optionally , an initial model is lytics module ( A.M. ) 704 accesses through various identi
provided for such mapping . Optionally , the ML module also fied third party and sources 708. In some embodiments , data
receives actual infection information , for example , by auto- 25 accessed from third – party databases and sources 708 may be
mated collection from medical records or from epidemio- analyzed and stored in Database Module ( DB.M ) 706 , thus
logical studies ( e.g. , of some or all infected people ) and uses supporting the simulations module 712 , which performs
this information to update the model , for example , using a machine – learning prediction activities . In some embodi
machine learning method as known in the art , to generate a ments , the analytics module ( A.M. ) 704 may also access
prediction of infectiveness ( and / or superspreader potential ) 30 data received from the simulations module 712 and previ
of an individual given his contacts and the superspreader ously stored in the Database Module ( DB.M ) 706 , thus
potential of similar individuals . In some embodiments of the benefiting from the machine learning and artificial intelli
invention , statistical methods are used instead of or in gence of the simulations module 712 .
addition to ML methods . Optionally or additionally , what is In some embodiments , the system optionally comprises a
created is a classifier , which classifies an individual as a 35 prediction module 714 with a prediction generator and in
potential superspreader . Such a classifier can build a clas- communication with the simulation module 712 and with the
sification scheme given a set of individuals , each with database module 706 .
behaviors and actual infectiveness as determined , for Not shown is a vaccination management server , which is
example , using epidemiological studies and / or contact track- optionally a separate component of the system or be a
ing combined with disease detection in such tracked con- 40 separate system . In some embodiments of the invention , this
tacts . Such classifier may be used ( or transmitted to indi- server is used to manage distribution of vaccinations ( e.g. ,
vidual devices to be used instead of and / or in addition to locations and / or times ) and / or tracking of subjects that
counting for example as described herein ) to generate a requested vaccination and / or received such vaccination .
general score for an individual based on the classification Optionally , this server manages the logistics of vaccine
and optionally based on additional information , such as 45 distribution using the information form the system indicat
medical risk . ing which subjects are to be vaccinated and in what order . In
Optionally or additionally , the AM includes one or more some embodiments of the invention , vaccinations are dis
optimization tools which given the various inputs described tributed based on population density and the vaccination
herein and / or one or more goals , optimizes vaccine delivery management server is used to track subjects receiving vac
and / or schedule to achieve a better approach to the goal . 50 cinations to ensure that they are not vaccinated out of turn ,
5. The analytics module ( A.M. ) serves the response back to for example , by comparing prioritization data provided by
the system , and transmits the list to the user , and the list is the devices against a record of prioritization intentions .
now available to the relevant personnel . In some embodi- In some embodiments , the system allows automatic
ments , this potentially helps the relevant personnel to decide machine learning as new data sources are added , and new
whom , when and where distribute the available doses to the 55 data is collected , and the predictive algorithms are recali population . brated and reselected using the expanded , and hence more
Each and any of such modules may be implemented , for reliable , data . In some embodiments , this may potentially
example , using a central server , a distributed server and / or a enable users of the system to quickly realize the value of
cloud implementation . new data .
In some embodiments , the system may automatically use 60 In some embodiments , the system utilizes machine learn
the simulation models to select and apply a predictive model ing , optionally incorporated in predictive model algorithms
for the preferred deployment of the doses ( for example , the to execute predictive analytical operations . Learning may be
parameter may be number of available doses or the higher supervised or unsupervised . In general , a predictive model
number of individuals protected by the act of vaccination analyzes historical data to identify patterns in the data . The
and / or a total number of expected of deaths and / or time to 65 patterns identified may include relationships between vari
reach a threshold where one or more limitations on society ous events , characteristics , or other attributes of the data
may be removed ) . In some embodiments , the system may being analyzed . Modeling of such patterns may provide a
10
20
25
US 11,107,588 B2
67 68
predictive model whereby predictions may be made . Devel- additionally , such device includes an ID generator . Option
opment of predictive models may employ mathematical or ally or additionally , such device includes communication
statistical modeling techniques such as curve fitting , software ( e.g. , addresses ) for making an anonymous drop of
smoothing , and regression analysis to fit or train the data . information and / or for receiving a general broadcast of
Such techniques may be used to model the distribution and 5 information ( e.g. , from the server ) and / or for accessing an
relationships of the variables , e.g. , how one or more events , individual’s EMR or other repository with relevant medical
characteristics , or circumstances ( which may be referred to information . Optionally or additionally , such a device
as “ independent variables ” or “ predictor variables ” ) relate to includes a count analysis and / or other module that applies a
an event or outcome ( which may be referred to as a classification or scoring method for example , as described
“ dependent variable ” or “ response ” ) . herein . Optionally or additionally , such a device includes a In some embodiments , a machine learning process may include developing a predictive model . For example, a sensor an associated software for detecting infection related dataset comprising observed data may be input into a information, for example, being indoors , location , distance modeling process for mapping of the variables within the from other electronic devices , duration at such distance , data . The mapped data may be used to develop a predictive 15 coughing sounds and / or video or still analysis to detect mask model . The machine learning process may also include wearing . Optionally or additionally, such a device includes utilizing the predictive model to make predictions regarding a display and associated software for showing a vaccination a specified outcome that is a dependent variable with respect invitation and / or a score . Optionally or additionally, such a
to the predictive model . The machine may then be provided device includes an input ( e.g. , a camera ) for receiving
an input of one or more observed predictor variables upon information form printed or other screens , for example, a which the output or response is requested . By executing the user’s occupation or special dispensation . Optionally or
machine – learning algorithm utilizing the input , the requested additionally , such device includes software , which generates
response may be generated and outputted . Thus , based on behavior alerts to the user , for example, when the user
the presence or occurrence of a known predictor variable , engages in riskier behavior .
the machine – learning algorithm may be used to predict a Various embodiments and aspects of the present invention related future event or the probability of the future event . as delineated hereinabove and as claimed in the claims
It is noted that a most basic prediction may be used , e.g. , section below find calculated support in the following
behavior in past predicts behavior in future . For example, if examples.
a person regularly meets 30 people a day for over 15 minutes EXAMPLE each and within 2 meters and I a location that is closed ( e.g. ,
based on mapping data sources ), it is assumed that may Reference is now made to the following prophetic continue . Similarly, if a person attends a church of 200 examples, which together with the above descriptions illus people once a week , that may be assumed to continue . In trate some embodiments of the invention in a non limiting addition , class behavior may be applied . For example , if the fashion . person is collage age , the system may be programmed with 35 In the following example, three imaginary individuals an expectation of a certain number and /o r expected dates ( John Doe , Jane Smith and Mark Lite ) will be scored and / or expected probability of parties such a person might according to one or more exemplary factors and / or compo attend . Such information may also be generate by statisti nents , as disclosed above . It should be understood that the cally analyzing the behavior of others in that person ‘ cohort . following scenario is not limiting and it is only provided to In some embodiments , once the treatment order list 716 is 40 enable a person having skills in the art to implement the ready , individual messages 718 are sent to the specific invention . individuals notifying them where and when they should go Background Information to be treated .
The architecture of the system may depend on the imple
mentation . For example , if the system is mainly anonymous ,
with scorings being generated on individual cellphones ( or
other devices ) , the server may be used to generate informa- Age ( relative weight 1 % ) tion to be used by the cellphones and / or to collate results Profession ( relative Operator Unemployed
weight 5 % ) generate vaccination prioritization plans and / or invite indi Known health conditions Chronic coughing
viduals to be vaccinated . ( relative weight 4 % )
In such an example , the software of the electronic device Visits religious gathering
may increase in relative importance . Such device may ( relative weight 20 % )
include a memory ( e.g. , as noted herein ) for storing actual
IDs or partial IDs and / or counts thereof . Optionally or Weekly Mobility Data
30
45
John Doe Jane Smith Mark Lite
30 35 33
Teacher
None None
50
No Yes Yes
John Doe Jane Smith Mark Lite
Day 1 Total locations visited : 5 Total locations visited : 3 Total locations visited : 1
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 650 on this day : 150 on this day : 5
Total locations visited : 6 Total locations visited : 4 Total locations visited : 1
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 750 on this day : 250 on this day : 5
Day 2
US 11,107,588 B2
69 70
-continued
John Doe Jane Smith Mark Lite
Day 3
Day 4
Day 5
Total locations visited : 5 Total locations visited : 2 Total locations visited : 2
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 650 on this day : 80 on this day : 30
Total locations visited : 5 Total locations visited : 2 Total locations visited : 1
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 650 on this day : 80 on this day : 5
Total locations visited : 5 Total locations visited : 3 Total locations visited : 2
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 650 on this day : 150 on this day : 30
Total locations visited : 5 Total locations visited : 1 Total locations visited : 1
Estimated potential Estimated potential Estimated potential
number of individuals number of individuals number of individuals
in contact with subject in contact with subject in contact with subject
on this day : 650 on this day : 5 on this day : 5
Total locations visited : 5 Total locations visited : 2 Total locations visited : 3
Estimated potential ( * visited Church ) ( * visited stadium )
number of individuals Estimated potential Estimated potential
in contact with subject number of individuals number of individuals
on this day : 650 in contact with subject in contact with subject
on this day : 500 on this day : 500
80 60 15
Day 6
Day 7
Score
( relative
weight 70 % )
John Doe Jane Smith
1 % 50
5 %
4 %
20 %
70 %
50
80
0
50
50
90
80
60
0
0
80
15
0
80
100 % 66.2 14.2
In view of the results of the Weekly mobility data alone , 30 dients , steps and / or parts do not materially alter the basic and
the order of the treatments will be John Doe , Jane Smith and novel characteristics of the claimed composition , method or
then Mark Lite . structure .
The calculation of the overall score is : As used herein , the singular forn “ a ” , “ an ” and ne ”
include plural references unless the context clearly dictates
35 otherwise . For example , the term “ a compound ” or “ at least
criteria Mark Lite one compound ” may include a plurality of compounds ,
including mixtures thereof . Age Throughout this application , embodiments of this inven Profession
Known health conditions tion may be presented with reference to a range format . It
Visits religious gathering 40 should be understood that the description in range format is
Mobility data merely for convenience and brevity and should not be
weighted scores 60.5 cinovnesnttriuoend . Aacsc oarnd iinngflleyx, i btlhee dleismcirtiapttiiono n oonf at hrea nsgceo pseh ouolfd t bhee
considered to have specifically disclosed all the possible
As can be seen , when taking under consideration all the 45 subranges as well as individual numerical values within that
information data , the order of the treatments will be Jane range . For example , description of a range such as “ from 1
Smith , John Doe and then Mark Lite . to 6 ” should be considered to have specifically disclosed
It should be understood that the above numeric examples subranges such as “ from 1 to 3 ” , “ from 1 to 4 ” , “ from 1 to
are just examples to help a person having skills in the art to 5 ” , “ from 2 to 4 ” , “ from 2 to 6 ” , “ from 3 to 6 ” , etc .; as well
understand the invention . It also should be understood that 50 as individual numbers within that range , for example , 1 , 2 ,
different weight values , scores and methods of calculating a 3 , 4 , 5 , and 6. This applies regardless of the breadth of the
score could be used . range .
It is expected that during the life of a patent maturing from Whenever a numerical range is indicated herein ( for
this application many relevant parameters of scoring activity example “ 10-15 ” , “ 10 to 15 ” , or any pair of numbers linked
of individuals and methods of measuring said parameters 55 by these another such range indication ) , it is meant to
will be developed ; the scope of the invention herein is include any number ( fractional or integral ) within the indi
intended to include all such new technologies a priori . cated range limits , including the range limits , unless the
As used herein with reference to quantity or value , the context clearly dictates otherwise . The phrases “ range / rang
term “ about ” means “ within + 20 % of ” . ing / ranges between ” a first indicate number and a second
The terms “ comprises ” , “ comprising ” , “ includes ” , 60 indicate number and “ range / ranging / ranges from ” a first
” including ” , ” has ” , “ having ” and their conjugates mean indicate number “ to ” , “ up to ” , “ until ” or “ through ” ( or
“ including but not limited to ” . another such range – indicating term ) a second indicate num
The term “ consisting of ” means “ including and limited ber are used herein interchangeably and are meant to include
to ” . the first and second indicated numbers and all the fractional
The term ” consisting essentially of ” means that the com- 65 and integral numbers therebetween .
position , method or structure may include additional ingre- Unless otherwise indicated , numbers used herein and any
dients , steps and / or parts , but only if the additional ingre- number ranges based thereon are approximations within the
US 11,107,588 B2
71 72
accuracy of reasonable measurement and rounding errors as 5. The method according to claim 1 , further comprising
understood by persons skilled in the art . using a second ID and transmitting said second ID or
It is appreciated that certain features of the invention , indication thereof together with said ID .
which are , for clarity , described in the context of separate 6. The method according to claim 5 , wherein said using a
embodiments , may also be provided in combination in a 5 second ID is carried out only at a fraction of said proximity
single embodiment . Conversely , various features of the events .
invention , which are , for brevity , described in the context of 7. The method according to claim 6 , wherein said using
a single embodiment , may also be provided separately or in comprises using also second IDs previously received from
any suitable subcombination or as suitable in any other others of said electronic devices .
described embodiment of the invention . Certain features 10 8. The method according to claim 6 , comprising gener
described in the context of various embodiments are not to ating an indication of closeness of a population met by said
be considered essential features of those embodiments , electronic device based on said received second IDs .
unless the embodiment is inoperative without those ele- 9. The method according to claim 1 , wherein said score
ments . depends on an estimation of propensity of proximity of said
It is the intent of the applicant ( s ) that all publications , 15 one or more other devices .
patents and patent applications referred to in this specifica- 10. The method according to claim 1 , wherein said
tion are to be incorporated in their entirety by reference into generating said score comprises counting the number of
the specification , as if each individual publication , patent or received IDs .
patent application was specifically and individually noted 11. The method according to claim 10 , wherein said
when referenced that it is to be incorporated herein by 20 counting comprises counting unique IDs .
reference . In addition , citation or identification of any ref- 12. The method according to claim 10 , wherein said
erence in this application shall not be construed as an counting comprises counting IDs with a weighted parameter ,
admission that such reference is available as prior art to the said weighted parameter is generated by analyzing said
present invention . To the extent that section headings are transmitted second IDs .
used , they should not be construed as necessarily limiting . In 25 13. The method according to claim 1 , wherein said
addition , any priority document ( s ) of this application is / are generating for each said plurality of subjects a prioritization
hereby incorporated herein by reference in its / their entirety . of vaccination comprises transmitting said score to a server
and generating said prioritization on said server .
What is claimed is : 14. The method according to claim 13 , wherein said
1. A method of prophylactically vaccinating a population 30 generating said prioritization comprises comparing scores
having a plurality of subjects with a vaccine against an by different ones of said electronic devices .
epidemic infectious disease , said plurality of subjects each 15. The method according to claim 1 , wherein said
using a sma electronic device , the method comprising : generating for each said plurality of subjects a prioritization
a . a . using an ID for each said smart electronic device for of vaccination comprises generating said prioritization on
determining a propensity of proximity of each said 35 said particular electronic device .
plurality of subjects ; said determining a propensity of 16. The method according to claim 15 , wherein said
proximity comprises : generating said prioritization comprises receiving from a
i . at a proximity event , when a particular said smart server a list or a function indication prioritization according
electronic device of a particular said subject is in
proximity of one or more other of said smart elec- 40 17. The method according to claim 1 , comprising dis
tronic devices , transmitting an ID or an indication playing prophylactically vaccinating instructions on said
thereof to said one or more other smart electronic particular electronic device based on said generated priori
devices and receiving an ID or indication thereof tization .
from said one or more other smart electronic devices , 18. The method of claim 1 , wherein said epidemic infec
by said particular smart electronic device ; 45 tious disease comprises a corona virus and wherein said
said proximity event being an event where said par- prophylactically vaccinating comprises a vaccination for
ticular said subject could , if infected , potentially said epidemic infectious disease and wherein said prioriti
infect other subjects with said infectious disease ; zation is used to select subjects at greater risk of transmitting
ii . generating a score reflecting a propensity for prox- said epidemic infectious disease during a pandemic to be
imity , according to a plurality of received IDs ; said 50 vaccinated sooner than subjects less likely to transmit said
propensity of proximity reflecting a chance of infect- epidemic infectious disease .
ing other subjects if said particular said subject 19. The method of claim 1 , wherein said ID is an
becomes infected ; anonymous ID .
b . generating for each said plurality of subjects a priori- 20. The method of claim 1 , wherein said ( a ) and ( b ) do not
tization of vaccination based on said score ; said priori- 55 comprise providing information regarding a status related to
tization being higher for subjects having a higher said infectious disease in said subjects .
propensity of proximity ; and 21. The method of claim 1 , wherein information about
c . prophylactically vaccinating particular subjects of said said prioritization of vaccination is not transmitted outside
plurality of subjects according to said prioritization . said particular electronic device .
2. The method according to claim 1 , wherein said using an 60 22. The method of claim 1 , wherein information about
ID comprises using an ID having fewer than 100,000 said proximity event is not transmitted outside said smart
potential values . electronic device or said one or more other smart electronic
3. The method according to claim 2 , wherein said using an devices .
ID comprises using a unique ID and also using said ID as a 23. A system for selecting subjects for prophylactically
portion of said unique ID . 65 vaccinating a population having a plurality of said subjects
4. The method according to claim 1 , further comprising with a vaccine against an epidemic infectious disease , com
changing said ID periodically . prising :
to score .
US 11,107,588 B2
73 74
a . a plurality of smart electronic devices configured to be 28. The system according to claim 23 , wherein said at
carried around by said subjects and configured with least one server or said smart electronic devices comprise
instructions to : instructions to determine a prophylactically vaccination pri
i . using an ID for each said smart electronic device for oritization based on said propensity for proximity .
determining a propensity of proximity of each said 5 plurality of subjects; said determining a propensity 29. The system according to claim 27 , wherein determine of proximity comprises: a treatment prioritization further comprises one or more of : at a proximity event, when in proximity of another a . generating a score component based on a nature of a such smart electronic device, transmitting an ID or location where said physical proximity data is related ; b . generating a score component comprising health data an indication thereof to said another smart elec- 10 of the subject of one or both smart electronic devices; tronic device and receive an ID or indication
thereof from said another smart electronic device ; c . generating a score component comprising a profession
said proximity event being an event where a of the subject of one or both smart electronic devices ;
particular said subject could , if infected , poten d . generating a score component reflecting relative health
tially infect other subjects with said infectious 15 risk to said subject if said subject contracts said epi
disease ; demic infectious disease ; and
generating a score reflecting a propensity for proximity , e . generating a score component reflecting damage to
according to a plurality of such received IDs ; said society if said subject contracts said epidemic infec
propensity of proximity reflecting a chance of infect tious disease .
ing other subjects if said particular said subject 20 30. The system according to claim 27 , wherein when said
becomes infected ; physical proximity data is related to a location that is either
receiving information from a server ; indoors or in a closed space , then said score of said subject
displaying relevant prophylactically vaccinating of transmitting said epidemic infectious disease increases by
instructions to said subjects based on said received a factor of between about 10 times to about 100 times .
information ; 31. The system according to claim 23 , further comprising
b . at least one server comprising a memory and a plurality a prophylactic vaccination server which allocates prophy
of modules ; said memory comprising instructions for : lactic vaccinations for a corona virus according to said
ii . sending to said plurality of smart electronic devices prophylactically vaccinating instructions .
information usable by a circuitry in said plurality of 32. The system according to claim 31 , wherein said server
smart electronic devices to display said relevant 30 comprises a simulation module configured to perform one or prophylactically vaccinating instructions. both of:
24. The system according to claim 23 , wherein said ( a ) predict the effect of vaccination on disease spread ;
information comprises one or more of subject specific ( b ) predict the effect of an ID transmission probability on
information . distinguishing between subjects who contact mainly
25. The system according to claim 23 , wherein said 35 subjects in a same subpopulation .
information comprises general information usable by a 33. The system of claim 23 , wherein said smart electronic
plurality of subjects and devices thereof . devices are configured to transmit a second ID and previ
26. The system according to claim 25 , wherein said server ously received second IDs , at a probability of less than 10 %
is configured with instructions to receive scores for a plu and using said received second IDs to generate said score .
rality of said electronic devices and use said received scores 40 34. The system of claim 23 , wherein said transmitted ID
to generate said general information , said electronic devices is a non – unique ID having fewer possible values than 10 %
configured to use said general information to determine a of the number of said devices .
relative treatment priority for their respective subjects . 35. The system of claim 23 , wherein said ID is an
27. The system according to claim 23 , wherein said smart anonymous ID .
electronic devices comprise a proximity – detecting module 45 36. The system of claim 23 , wherein said plurality of
using one or more of : smart electronic devices do not comprise information
a . physical proximity data received by means of electronic regarding a status related to said infectious disease in said
positioning data of said subject ; subjects .
b . a distance indicating sensor which indicates physical 37. The system according to claim 23 , wherein informa
proximity of the location of a device in relation to the 50 tion about said prophylactically vaccinating instructions is
location of said another device ; and not transmitted outside a particular smart

Um höfund

Sigurlaug Ragnarsdóttir

✞༺(((( Ⓒilla ℜągnąℜṧ )))༻♚༺ BA Classical Art Historian || MA Culture & Media || Tourism & Sales Management || Web Design || Photo & Videographer for Tourism Magasins ༻

Síðustu færslur

• GLÆPUR GEGN MANNKYNI9. janúar, 2024Janet Ossebaard, höfundur heimildarmyndarinnar “The fall of the Cabal” fannst látin á jóladag
• Sameinuðu Þjóðirnar28. desember, 2023Lestrarefni Menntamálastofnunar: Varúð hér býr vampíra – auðlesin sögubók á léttu máli og bókinni fylgir verkefnahefti sem hægt er að vinna samhliða lestri.
• Heimsmarkmiðin27. desember, 2023Hin raunverulega merking og innhald 17 Heimsmarkmiða (UN) Sameinuðu þjóðanna
• Óflokkað20. desember, 2023Hvað varð um Tinnu og hver er staða hennar í dag varðandi þá lömun sem hún varð fyrir neðan mitti eftir Moderna bólusetninguna?