The landscape of Artificial Intelligence Models and Applications for Epidemic Outbreaks
Main Article Content
Abstract
The epidemiology has recently witnessed great advances based on computational models. Its scope and impact are getting wider thanks to the new data sources feeding analytical frameworks and models. Besides traditional variables considered in epidemiology, large-scale social patterns can be now integrated in real time with multi-source data bridging the gap between different scales. In a hyper-connected world, models and analysis of interactions and social behaviors are key to understand and stop outbreaks. Big Data along with apps are enabling for validating and refining models with real world data at scale, as well as new applications and frameworks to map and track diseases in real time or optimize the necessary resources and interventions such as testing and vaccination strategies. Digital epidemiology is positioning itself as a discipline necessary to control epidemics and implement actionable protocols and policies. In this review we address the research areas configuring current digital epidemiology: transmission and propagation models and descriptions based on human networks and contact tracing, mobility analysis and spatio-temporal propagation of infectious diseases and infodemics that comprises the study of information and knowledge propagation. Digital epidemiology has the potential to create new operational mechanisms for prevention and mitigation, monitoring of the evolution of epidemics, assessing their impact and evaluating the pharmaceutical and non-pharmaceutical measures to fight the outbreaks. Epidemics have to be approached from the lens of complexity science as they require systemic solutions. Opportunities and challenges to tackle epidemics more effectively and with a human-centered vision are discussed here.
Keywords:
Digital epidemiology, contact tracing, networks, disease propagation, epidemiological impact, infodemics, human mobility, Big Data, epidemiological models, privacy, ethics, resilience
Article Details
How to Cite
PASTOR-ESCUREDO, David; OLMOS, Luis E..
The landscape of Artificial Intelligence Models and Applications for Epidemic Outbreaks.
Medical Research Archives, [S.l.], v. 10, n. 10, oct. 2022.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/3175>. Date accessed: 24 apr. 2024.
doi: https://doi.org/10.18103/mra.v10i10.3175.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Green MD, Freedman DM, Gordis L. Reference guide on epidemiology. Reference Manual on Scientific Evidence. 2000;2:638.
2. Salathe M, Bengtsson L, Bodnar TJ, et al. Digital epidemiology. PLoS computational biology. 2012;8(7)
3. Wormser GP, Pourbohloul B. Modeling Infectious Diseases in Humans and Animals By Matthew James Keeling and Pejman Rohani Princeton, NJ: Princeton University Press, 2008. 408 pp., Illustrated. $65.00 (hardcover). The University of Chicago Press; 2008.
4. Anderson RM, Anderson B, May RM. Infectious diseases of humans: dynamics and control. Oxford university press; 1992.
5. Bell G, Hey T, Szalay A. Beyond the data deluge. Science. 2009;323(5919):1297-1298.
6. Bullock J, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv:200311336. 2020;
7. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-444.
8. Kumar A, Gupta PK, Srivastava A. A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020;14(4):569-573.
9. Shorten C, Khoshgoftaar TM, Furht B. Deep Learning applications for COVID-19. Journal of big Data. 2021;8(1):1-54.
10. Hernán MA, Robins JM. Estimating causal effects from epidemiological data. Journal of Epidemiology & Community Health. 2006;60(7):578-586.
11. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of medical Internet research. 2009;11(1):e11.
12. Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. Journal of epidemiology and global health. 2017;7(3):185-189.
13. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection—harnessing the Web for public health surveillance. The New England journal of medicine. 2009;360(21):2153.
14. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012-1014.
15. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203-1205.
16. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS computational biology. 2013;9(10)
17. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PloS one. 2011;6(8)
18. Salathé M. Digital epidemiology: what is it, and where is it going? Life sciences, society and policy. 2018;14(1):1.
19. Vespignani A, Tian H, Dye C, et al. Modelling COVID-19. Nature Reviews Physics. 2020:1-3.
20. Aleta A, Martín-Corral D, y Piontti AP, et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour. 2020:1-8.
21. Espana G, Cavany S, Oidtman RJ, et al. Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA. medRxiv. 2020;
22. Moreno López JA, Arregui García B, Bentkowski P, et al. Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection. Science advances. 2021;7(15):eabd8750.
23. Hinch R, Probert WJ, Nurtay A, et al. OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. PLoS computational biology. 2021;17(7):e1009146.
24. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Physical review letters. 2001;86(14):3200.
25. Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic outbreaks in complex heterogeneous networks. The European Physical Journal B-Condensed Matter and Complex Systems. 2002;26(4):521-529.
26. de Arruda GF, Petri G, Rodrigues FA, Moreno Y. Impact of the distribution of recovery rates on disease spreading in complex networks. Physical Review Research. 2020;2(1):013046.
27. Pastor-Satorras R, Vespignani A. Epidemic dynamics in finite size scale-free networks. Physical Review E. 2002;65(3):035108.
28. Pastor-Satorras R, Vespignani A. Epidemic dynamics and endemic states in complex networks. Physical Review E. 2001;63(6):066117.
29. May RM, Lloyd AL. Infection dynamics on scale-free networks. Physical Review E. 2001;64(6):066112.
30. Stauffer D, Aharony A. Introduction to percolation theory. Taylor & Francis; 2018.
31. Newman ME. Spread of epidemic disease on networks. Physical review E. 2002;66(1):016128.
32. Meyers LA, Newman M, Pourbohloul B. Predicting epidemics on directed contact networks. Journal of theoretical biology. 2006;240(3):400-418.
33. McMahon T, Chan A, Havlin S, Gallos LK. Spatial correlations in geographical spreading of COVID-19 in the United States. Scientific reports. 2022;12(1):1-10.
34. Colizza V, Vespignani A. Invasion threshold in heterogeneous metapopulation networks. Physical review letters. 2007;99(14):148701.
35. de Arruda GF, Petri G, Moreno Y. Social contagion models on hypergraphs. Physical Review Research. 2020;2(2):023032.
36. Colizza V, Barrat A, Barthelemy M, Valleron A-J, Vespignani A. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS medicine. 2007;4(1)
37. Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395-400.
38. Zhang J, Litvinova M, Liang Y, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;
39. Martín-Calvo D, Aleta A, Pentland A, Moreno Y, Moro E. Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. 2020.
40. Ferretti L, Wymant C, Kendall M, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491)
41. Boy J, Pastor-Escuredo D, Macguire D, Jimenez RM, Luengo-Oroz M. Towards an understanding of refugee segregation, isolation, homophily and ultimately integration in Turkey using call detail records. Guide to Mobile Data Analytics in Refugee Scenarios. Springer; 2019:141-164.
42. Sanz J, Xia C-Y, Meloni S, Moreno Y. Dynamics of interacting diseases. Physical Review X. 2014;4(4):041005.
43. Prothero RM. Disease and mobility: a neglected factor in epidemiology. International journal of epidemiology. 1977;6(3):259-267.
44. Gonzalez MC, Hidalgo CA, Barabasi A-L. Understanding individual human mobility patterns. nature. 2008;453(7196):779-782.
45. Riley S. Large-scale spatial-transmission models of infectious disease. Science. 2007;316(5829):1298-1301.
46. Eubank S, Guclu H, Kumar VA, et al. Modelling disease outbreaks in realistic urban social networks. Nature. 2004;429(6988):180-184.
47. Candia J, González MC, Wang P, Schoenharl T, Madey G, Barabási A-L. Uncovering individual and collective human dynamics from mobile phone records. Journal of physics A: mathematical and theoretical. 2008;41(22):224015.
48. Zufiria PJ, Pastor-Escuredo D, Úbeda-Medina L, et al. Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security. PloS one. 2018;13(4):e0195714.
49. Pastor-Escuredo D, Frias-Martinez E. Flow descriptors of human mobility networks. arXiv preprint arXiv:200307279. 2020;
50. Balcan D, Colizza V, Gonçalves B, Hu H, Ramasco JJ, Vespignani A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences. 2009;106(51):21484-21489.
51. Chang S, Pierson E, Koh PW, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589(7840):82-87.
52. Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. Journal of computational science. 2010;1(3):132-145.
53. Balcan D, Hu H, Goncalves B, et al. Seasonal transmission potential and activity peaks of the new influenza A (H1N1): a Monte Carlo likelihood analysis based on human mobility. BMC medicine. 2009;7(1):45.
54. Simini F, González MC, Maritan A, Barabási A-L. A universal model for mobility and migration patterns. Nature. 2012;484(7392):96.
55. Perrota D. Can Mobile Phone Traces Help Shed Light on the Spread of Zika in Colombia? UNGP. 2020. 2018. https://www.unglobalpulse.org/2018/04/can-mobile-phone-traces-help-shed-light-on-the-spread-of-zika-in-colombia/
56. Stoddard ST, Morrison AC, Vazquez-Prokopec GM, et al. The role of human movement in the transmission of vector-borne pathogens. PLoS neglected tropical diseases. 2009;3(7)
57. Lynch C, Roper C. The transit phase of migration: circulation of malaria and its multidrug-resistant forms in Africa. PLoS medicine. 2011;8(5)
58. Buscarino A, Fortuna L, Frasca M, Latora V. Disease spreading in populations of moving agents. EPL (Europhysics Letters). 2008;82(3):38002.
59. Meloni S, Perra N, Arenas A, Gómez S, Moreno Y, Vespignani A. Modeling human mobility responses to the large-scale spreading of infectious diseases. Scientific reports. 2011;1:62.
60. Blondel VD, Decuyper A, Krings G. A survey of results on mobile phone datasets analysis. EPJ data science. 2015;4(1):10.
61. Pulse UG. Big data for development: Challenges & opportunities. Naciones Unidas, Nueva York, mayo. 2012;
62. Pulse UG. Mapping the Risk-Utility Landscape: Mobile Data for Sustainable Development and Humanitarian Action. Global Pulse Project Series no18. 2015;
63. De Montjoye Y-A, Hidalgo CA, Verleysen M, Blondel VD. Unique in the crowd: The privacy bounds of human mobility. Scientific reports. 2013;3:1376.
64. Grantz KH, Meredith HR, Cummings DA, et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nature communications. 2020;11(1):1-8.
65. Bejon P, Williams TN, Liljander A, et al. Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS medicine. 2010;7(7)
66. Dolgin E. Targeting hotspots of transmission promises to reduce malaria. Nature Publishing Group; 2010.
67. Wesolowski A, Eagle N, Tatem AJ, et al. Quantifying the impact of human mobility on malaria. Science. 2012;338(6104):267-270.
68. Bengtsson L, Lu X, Thorson A, Garfield R, Von Schreeb J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS medicine. 2011;8(8)
69. Wesolowski A, Qureshi T, Boni MF, et al. Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences. 2015;112(38):11887-11892.
70. Bengtsson L, Gaudart J, Lu X, et al. Using mobile phone data to predict the spatial spread of cholera. Scientific reports. 2015;5:8923.
71. Tizzoni M, Bajardi P, Decuyper A, et al. On the use of human mobility proxies for modeling epidemics. PLoS computational biology. 2014;10(7)
72. Tatem AJ, Huang Z, Narib C, et al. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria journal. 2014;13(1):52.
73. Oliver N, Lepri B, Sterly H, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. American Association for the Advancement of Science; 2020. p. eabc0764.
74. Adam DC, Wu P, Wong JY, et al. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine. 2020;26(11):1714-1719.
75. Finger F, Genolet T, Mari L, et al. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. Proceedings of the National Academy of Sciences. 2016;113(23):6421-6426.
76. UNICEF. Magic Box COVID-19 report. UNICEF. 2020. https://www.unicef.org/innovation/magicbox/covid
77. Dueñas M, Campi M, Olmos L. Changes in mobility and socioeconomic conditions in Bogotá city during the COVID-19 outbreak. arXiv preprint arXiv:200811850. 2020;
78. Gozzi N, Tizzoni M, Chinazzi M, Ferres L, Vespignani A, Perra N. Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile. Nature communications. 2021;12(1):1-9.
79. Mena GE, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science. 2021;372(6545):eabg5298.
80. Belderok S-M, Rimmelzwaan GF, Van Den Hoek A, Sonder GJ. Effect of travel on influenza epidemiology. Emerging infectious diseases. 2013;19(6):925.
81. Wesolowski A, Stresman G, Eagle N, et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Scientific reports. 2014;4:5678.
82. Oliver N, Lepri B, Sterly H, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. American Association for the Advancement of Science; 2020.
83. Ventura PC, Aleta A, Rodrigues FA, Moreno Y. Modeling the effects of social distancing on the large-scale spreading of diseases. Epidemics. 2022;38:100544.
84. Taleb NN. The Statistical Consequences of Fat Tails. STEM Publishing; 2019.
85. Taleb NN, Bar-Yam Y, Douady R, Norman J, Read R. The precautionary principle: fragility and black swans from policy actions. NYU Extreme Risk Initiative Working Paper. 2014:1-24.
86. Norman J, Bar-Yam Y, Taleb NN. Systemic Risk of Pandemic via Novel Pathogens—Coronavirus: A Note. New England Complex Systems Institute (January 26, 2020). 2020;
87. Greenwood F, Howarth C, Escudero Poole D, Raymond NA, Scarnecchia DP. The signal code: A human rights approach to information during crisis. Harvard, MA. 2017;
88. General US. Good communication saves lives. 2020. 2020. https://www.un.org/en/coronavirus/good-communication-saves-lives
89. Lazer DM, Baum MA, Benkler Y, et al. The science of fake news. Science. 2018;359(6380):1094-1096.
90. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter. 2017;19(1):22-36.
91. Bakir V, McStay A. Fake news and the economy of emotions: Problems, causes, solutions. Digital journalism. 2018;6(2):154-175.
92. Zarocostas J. How to fight an infodemic. The Lancet. 2020;395(10225):676.
93. Vaezi A, Javanmard SH. Infodemic and risk communication in the era of CoV-19. Advanced Biomedical Research. 2020;9
94. Hua J, Shaw R. Corona virus (Covid-19)“infodemic” and emerging issues through a data lens: The case of china. International journal of environmental research and public health. 2020;17(7):2309.
95. Nekovee M, Moreno Y, Bianconi G, Marsili M. Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and its Applications. 2007;374(1):457-470.
96. Miritello G, Moro E, Lara R. Dynamical strength of social ties in information spreading. Physical Review E. 2011;83(4):045102.
97. Morales AJ, Borondo J, Losada JC, Benito RM. Efficiency of human activity on information spreading on Twitter. Social Networks. 2014;39:1-11.
98. Bodendorf F, Kaiser C. Detecting opinion leaders and trends in online social networks. 2009:65-68.
99. Pastor-Escuredo D, Tarazona C. Characterizing information leaders in Twitter during COVID-19 crisis. arXiv preprint arXiv:200507266. 2020;
100. Newman ME, Barabási A-LE, Watts DJ. The structure and dynamics of networks. Princeton university press; 2006.
101. Borge-Holthoefer J, Meloni S, Gonçalves B, Moreno Y. Emergence of influential spreaders in modified rumor models. Journal of Statistical Physics. 2013;151(1-2):383-393.
102. Popat K, Mukherjee S, Yates A, Weikum G. DeClarE: Debunking fake news and false claims using evidence-aware deep learning. arXiv preprint arXiv:180906416. 2018;
103. Ruchansky N, Seo S, Liu Y. Csi: A hybrid deep model for fake news detection. 2017:797-806.
104. Singhania S, Fernandez N, Rao S. 3han: A deep neural network for fake news detection. Springer; 2017:572-581.
105. Luengo-Oroz M, Pham KH, Bullock J, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nature Machine Intelligence. 2020:1-3.
106. Leung GM, Leung K. Crowdsourcing data to mitigate epidemics. The Lancet Digital Health. 2020;2(4):e156-e157.
107. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital Health. 2020;
108. Imran A, Posokhova I, Qureshi HN, et al. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked. 2020:100378.
109. Menni C, Valdes A, Freydin MB, et al. Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection. MedRxiv. 2020;
110. Carrillo-Larco RM, Castillo-Cara M. Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Research. 2020;5(56):56.
111. Hu Z, Ge Q, Jin L, Xiong M. Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:200207112. 2020;
112. Hartono P. Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder. arXiv preprint arXiv:200401481. 2020;
113. Liu D, Clemente L, Poirier C, et al. A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv preprint arXiv:200404019. 2020;
114. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology). 2005;67(2):301-320.
115. Bandyopadhyay SK, Dutta S. Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release. medRxiv. 2020;
116. Huang C-J, Chen Y-H, Ma Y, Kuo P-H. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. medRxiv. 2020;
117. Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. arXiv preprint arXiv:200310776. 2020;
118. Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature communications. 2019;10(1):1-10.
119. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences. 2015;112(47):14473-14478.
120. Rajarethinam J, Ong J, Lim S-H, et al. Using human movement data to identify potential areas of Zika transmission: case study of the largest Zika cluster in Singapore. International journal of environmental research and public health. 2019;16(5):808.
121. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance. 2020;25(10):2000180.
122. Moghadas SM, Fitzpatrick MC, Sah P, et al. The implications of silent transmission for the control of COVID-19 outbreaks. Proceedings of the National Academy of Sciences. 2020;117(30):17513-17515.
123. Kermali M, Khalsa RK, Pillai K, Ismail Z, Harky A. The role of biomarkers in diagnosis of COVID-19–A systematic review. Life Sciences. 2020:117788.
124. Shi Y, Wang Y, Shao C, et al. COVID-19 infection: the perspectives on immune responses. Nature Publishing Group; 2020.
125. Nicholas GD, Petra K, Yang L, et al. Age-dependent Effects in the Transmission and Control of COVID-19 Epidemics. Nature medicine. 2020;
126. Eames KT, Keeling MJ. Contact tracing and disease control. Proceedings of the Royal Society of London Series B: Biological Sciences. 2003;270(1533):2565-2571.
127. Dietz K. The estimation of the basic reproduction number for infectious diseases. Statistical methods in medical research. 1993;2(1):23-41.
128. Liu Q-H, Ajelli M, Aleta A, Merler S, Moreno Y, Vespignani A. Measurability of the epidemic reproduction number in data-driven contact networks. Proceedings of the National Academy of Sciences. 2018;115(50):12680-12685.
129. Glass RJ, Glass LM, Beyeler WE, Min HJ. Targeted social distancing designs for pandemic influenza. Emerging infectious diseases. 2006;12(11):1671.
130. Lampos V, Moura S, Yom-Tov E, Cox IJ, McKendry R, Edelstein M. Tracking COVID-19 using online search. arXiv preprint arXiv:200308086. 2020;
131. WHO. Infodemic management - Infodemiology;. WHO. 2020. https://www.who.int/teams/risk-communication/infodemic-management.
132. Singh L, Bansal S, Bode L, et al. A first look at COVID-19 information and misinformation sharing on Twitter. arXiv preprint arXiv:200313907. 2020;
133. Mejova Y, Kalimeri K. Advertisers jump on coronavirus bandwagon: Politics, news, and business. arXiv preprint arXiv:200300923. 2020;
134. Hu Z, Ge Q, Li S, Boerwincle E, Jin L, Xiong M. Forecasting and evaluating intervention of Covid-19 in the World. arXiv preprint arXiv:200309800. 2020;
135. Dandekar R, Barbastathis G. Neural Network aided quarantine control model estimation of global Covid-19 spread. arXiv preprint arXiv:200402752. 2020;
136. Lai S, Ruktanonchai NW, Zhou L, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. 2020;
137. Flaxman S, Mishra S, Gandy A, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020:1-5.
138. Davies NG, Kucharski AJ, Eggo RM, et al. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. The Lancet Public Health. 2020;
139. Cowling BJ, Ali ST, Ng TW, et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. The Lancet Public Health. 2020;
140. Taleb NN, Read R, Douady R, Norman J, Bar-Yam Y. The precautionary principle (with application to the genetic modification of organisms). arXiv preprint arXiv:14105787. 2014;
141. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438(7066):355-359.
142. Kupferschmidt K. Why do some COVID-19 patients infect many others, whereas most don’t spread the virus at all? Science. 19/05/2020 2020;
143. Mello MM, Wang CJ. Ethics and governance for digital disease surveillance. Science. 2020;
144. Kuhn C, Beck M, Strufe T. Covid Notions: Towards Formal Definitions--and Documented Understanding--of Privacy Goals and Claimed Protection in Proximity-Tracing Services. arXiv preprint arXiv:200407723. 2020;
145. Vinuesa R, Theodorou A, Battaglini M, Dignum V. A socio-technical framework for digital contact tracing. arXiv preprint arXiv:200508370. 2020;
146. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases. 2020;20(5):533-534.
147. Aleks Berditchevskaia KP. Coronavirus: seven ways collective intelligence is tackling the pandemic. The Conversation. 2020. 2020. https://theconversation.com/coronavirus-seven-ways-collective-intelligence-is-tackling-the-pandemic-133553
148. Pastor-Escuredo D, Tarazona-Lizarraga C, Bachmann A, Treleaven P. Collective Intelligence and Governance for Pandemics. Covid-19: A Complex Systems Approach. STEM Academic Press; 2021:223-248.
149. Pastor-Escuredo D. Ethics in the digital era. arXiv preprint arXiv:200306530. 2020;
150. Berman G, Carter K, Herranz MG, Sekara V. Digital contact tracing and surveillance during COVID-19. General and child-specific ethical issues. 2020.
151. General US. The age of digital interdependence. Report of the UN Secretary-General’s High-Level Panel on Digital Cooperation …; 2019.
2. Salathe M, Bengtsson L, Bodnar TJ, et al. Digital epidemiology. PLoS computational biology. 2012;8(7)
3. Wormser GP, Pourbohloul B. Modeling Infectious Diseases in Humans and Animals By Matthew James Keeling and Pejman Rohani Princeton, NJ: Princeton University Press, 2008. 408 pp., Illustrated. $65.00 (hardcover). The University of Chicago Press; 2008.
4. Anderson RM, Anderson B, May RM. Infectious diseases of humans: dynamics and control. Oxford university press; 1992.
5. Bell G, Hey T, Szalay A. Beyond the data deluge. Science. 2009;323(5919):1297-1298.
6. Bullock J, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv:200311336. 2020;
7. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-444.
8. Kumar A, Gupta PK, Srivastava A. A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020;14(4):569-573.
9. Shorten C, Khoshgoftaar TM, Furht B. Deep Learning applications for COVID-19. Journal of big Data. 2021;8(1):1-54.
10. Hernán MA, Robins JM. Estimating causal effects from epidemiological data. Journal of Epidemiology & Community Health. 2006;60(7):578-586.
11. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of medical Internet research. 2009;11(1):e11.
12. Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. Journal of epidemiology and global health. 2017;7(3):185-189.
13. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection—harnessing the Web for public health surveillance. The New England journal of medicine. 2009;360(21):2153.
14. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012-1014.
15. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203-1205.
16. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS computational biology. 2013;9(10)
17. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PloS one. 2011;6(8)
18. Salathé M. Digital epidemiology: what is it, and where is it going? Life sciences, society and policy. 2018;14(1):1.
19. Vespignani A, Tian H, Dye C, et al. Modelling COVID-19. Nature Reviews Physics. 2020:1-3.
20. Aleta A, Martín-Corral D, y Piontti AP, et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour. 2020:1-8.
21. Espana G, Cavany S, Oidtman RJ, et al. Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA. medRxiv. 2020;
22. Moreno López JA, Arregui García B, Bentkowski P, et al. Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection. Science advances. 2021;7(15):eabd8750.
23. Hinch R, Probert WJ, Nurtay A, et al. OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. PLoS computational biology. 2021;17(7):e1009146.
24. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Physical review letters. 2001;86(14):3200.
25. Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic outbreaks in complex heterogeneous networks. The European Physical Journal B-Condensed Matter and Complex Systems. 2002;26(4):521-529.
26. de Arruda GF, Petri G, Rodrigues FA, Moreno Y. Impact of the distribution of recovery rates on disease spreading in complex networks. Physical Review Research. 2020;2(1):013046.
27. Pastor-Satorras R, Vespignani A. Epidemic dynamics in finite size scale-free networks. Physical Review E. 2002;65(3):035108.
28. Pastor-Satorras R, Vespignani A. Epidemic dynamics and endemic states in complex networks. Physical Review E. 2001;63(6):066117.
29. May RM, Lloyd AL. Infection dynamics on scale-free networks. Physical Review E. 2001;64(6):066112.
30. Stauffer D, Aharony A. Introduction to percolation theory. Taylor & Francis; 2018.
31. Newman ME. Spread of epidemic disease on networks. Physical review E. 2002;66(1):016128.
32. Meyers LA, Newman M, Pourbohloul B. Predicting epidemics on directed contact networks. Journal of theoretical biology. 2006;240(3):400-418.
33. McMahon T, Chan A, Havlin S, Gallos LK. Spatial correlations in geographical spreading of COVID-19 in the United States. Scientific reports. 2022;12(1):1-10.
34. Colizza V, Vespignani A. Invasion threshold in heterogeneous metapopulation networks. Physical review letters. 2007;99(14):148701.
35. de Arruda GF, Petri G, Moreno Y. Social contagion models on hypergraphs. Physical Review Research. 2020;2(2):023032.
36. Colizza V, Barrat A, Barthelemy M, Valleron A-J, Vespignani A. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS medicine. 2007;4(1)
37. Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395-400.
38. Zhang J, Litvinova M, Liang Y, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;
39. Martín-Calvo D, Aleta A, Pentland A, Moreno Y, Moro E. Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. 2020.
40. Ferretti L, Wymant C, Kendall M, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491)
41. Boy J, Pastor-Escuredo D, Macguire D, Jimenez RM, Luengo-Oroz M. Towards an understanding of refugee segregation, isolation, homophily and ultimately integration in Turkey using call detail records. Guide to Mobile Data Analytics in Refugee Scenarios. Springer; 2019:141-164.
42. Sanz J, Xia C-Y, Meloni S, Moreno Y. Dynamics of interacting diseases. Physical Review X. 2014;4(4):041005.
43. Prothero RM. Disease and mobility: a neglected factor in epidemiology. International journal of epidemiology. 1977;6(3):259-267.
44. Gonzalez MC, Hidalgo CA, Barabasi A-L. Understanding individual human mobility patterns. nature. 2008;453(7196):779-782.
45. Riley S. Large-scale spatial-transmission models of infectious disease. Science. 2007;316(5829):1298-1301.
46. Eubank S, Guclu H, Kumar VA, et al. Modelling disease outbreaks in realistic urban social networks. Nature. 2004;429(6988):180-184.
47. Candia J, González MC, Wang P, Schoenharl T, Madey G, Barabási A-L. Uncovering individual and collective human dynamics from mobile phone records. Journal of physics A: mathematical and theoretical. 2008;41(22):224015.
48. Zufiria PJ, Pastor-Escuredo D, Úbeda-Medina L, et al. Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security. PloS one. 2018;13(4):e0195714.
49. Pastor-Escuredo D, Frias-Martinez E. Flow descriptors of human mobility networks. arXiv preprint arXiv:200307279. 2020;
50. Balcan D, Colizza V, Gonçalves B, Hu H, Ramasco JJ, Vespignani A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences. 2009;106(51):21484-21489.
51. Chang S, Pierson E, Koh PW, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589(7840):82-87.
52. Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. Journal of computational science. 2010;1(3):132-145.
53. Balcan D, Hu H, Goncalves B, et al. Seasonal transmission potential and activity peaks of the new influenza A (H1N1): a Monte Carlo likelihood analysis based on human mobility. BMC medicine. 2009;7(1):45.
54. Simini F, González MC, Maritan A, Barabási A-L. A universal model for mobility and migration patterns. Nature. 2012;484(7392):96.
55. Perrota D. Can Mobile Phone Traces Help Shed Light on the Spread of Zika in Colombia? UNGP. 2020. 2018. https://www.unglobalpulse.org/2018/04/can-mobile-phone-traces-help-shed-light-on-the-spread-of-zika-in-colombia/
56. Stoddard ST, Morrison AC, Vazquez-Prokopec GM, et al. The role of human movement in the transmission of vector-borne pathogens. PLoS neglected tropical diseases. 2009;3(7)
57. Lynch C, Roper C. The transit phase of migration: circulation of malaria and its multidrug-resistant forms in Africa. PLoS medicine. 2011;8(5)
58. Buscarino A, Fortuna L, Frasca M, Latora V. Disease spreading in populations of moving agents. EPL (Europhysics Letters). 2008;82(3):38002.
59. Meloni S, Perra N, Arenas A, Gómez S, Moreno Y, Vespignani A. Modeling human mobility responses to the large-scale spreading of infectious diseases. Scientific reports. 2011;1:62.
60. Blondel VD, Decuyper A, Krings G. A survey of results on mobile phone datasets analysis. EPJ data science. 2015;4(1):10.
61. Pulse UG. Big data for development: Challenges & opportunities. Naciones Unidas, Nueva York, mayo. 2012;
62. Pulse UG. Mapping the Risk-Utility Landscape: Mobile Data for Sustainable Development and Humanitarian Action. Global Pulse Project Series no18. 2015;
63. De Montjoye Y-A, Hidalgo CA, Verleysen M, Blondel VD. Unique in the crowd: The privacy bounds of human mobility. Scientific reports. 2013;3:1376.
64. Grantz KH, Meredith HR, Cummings DA, et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nature communications. 2020;11(1):1-8.
65. Bejon P, Williams TN, Liljander A, et al. Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS medicine. 2010;7(7)
66. Dolgin E. Targeting hotspots of transmission promises to reduce malaria. Nature Publishing Group; 2010.
67. Wesolowski A, Eagle N, Tatem AJ, et al. Quantifying the impact of human mobility on malaria. Science. 2012;338(6104):267-270.
68. Bengtsson L, Lu X, Thorson A, Garfield R, Von Schreeb J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS medicine. 2011;8(8)
69. Wesolowski A, Qureshi T, Boni MF, et al. Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences. 2015;112(38):11887-11892.
70. Bengtsson L, Gaudart J, Lu X, et al. Using mobile phone data to predict the spatial spread of cholera. Scientific reports. 2015;5:8923.
71. Tizzoni M, Bajardi P, Decuyper A, et al. On the use of human mobility proxies for modeling epidemics. PLoS computational biology. 2014;10(7)
72. Tatem AJ, Huang Z, Narib C, et al. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria journal. 2014;13(1):52.
73. Oliver N, Lepri B, Sterly H, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. American Association for the Advancement of Science; 2020. p. eabc0764.
74. Adam DC, Wu P, Wong JY, et al. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nature Medicine. 2020;26(11):1714-1719.
75. Finger F, Genolet T, Mari L, et al. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. Proceedings of the National Academy of Sciences. 2016;113(23):6421-6426.
76. UNICEF. Magic Box COVID-19 report. UNICEF. 2020. https://www.unicef.org/innovation/magicbox/covid
77. Dueñas M, Campi M, Olmos L. Changes in mobility and socioeconomic conditions in Bogotá city during the COVID-19 outbreak. arXiv preprint arXiv:200811850. 2020;
78. Gozzi N, Tizzoni M, Chinazzi M, Ferres L, Vespignani A, Perra N. Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile. Nature communications. 2021;12(1):1-9.
79. Mena GE, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science. 2021;372(6545):eabg5298.
80. Belderok S-M, Rimmelzwaan GF, Van Den Hoek A, Sonder GJ. Effect of travel on influenza epidemiology. Emerging infectious diseases. 2013;19(6):925.
81. Wesolowski A, Stresman G, Eagle N, et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Scientific reports. 2014;4:5678.
82. Oliver N, Lepri B, Sterly H, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. American Association for the Advancement of Science; 2020.
83. Ventura PC, Aleta A, Rodrigues FA, Moreno Y. Modeling the effects of social distancing on the large-scale spreading of diseases. Epidemics. 2022;38:100544.
84. Taleb NN. The Statistical Consequences of Fat Tails. STEM Publishing; 2019.
85. Taleb NN, Bar-Yam Y, Douady R, Norman J, Read R. The precautionary principle: fragility and black swans from policy actions. NYU Extreme Risk Initiative Working Paper. 2014:1-24.
86. Norman J, Bar-Yam Y, Taleb NN. Systemic Risk of Pandemic via Novel Pathogens—Coronavirus: A Note. New England Complex Systems Institute (January 26, 2020). 2020;
87. Greenwood F, Howarth C, Escudero Poole D, Raymond NA, Scarnecchia DP. The signal code: A human rights approach to information during crisis. Harvard, MA. 2017;
88. General US. Good communication saves lives. 2020. 2020. https://www.un.org/en/coronavirus/good-communication-saves-lives
89. Lazer DM, Baum MA, Benkler Y, et al. The science of fake news. Science. 2018;359(6380):1094-1096.
90. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter. 2017;19(1):22-36.
91. Bakir V, McStay A. Fake news and the economy of emotions: Problems, causes, solutions. Digital journalism. 2018;6(2):154-175.
92. Zarocostas J. How to fight an infodemic. The Lancet. 2020;395(10225):676.
93. Vaezi A, Javanmard SH. Infodemic and risk communication in the era of CoV-19. Advanced Biomedical Research. 2020;9
94. Hua J, Shaw R. Corona virus (Covid-19)“infodemic” and emerging issues through a data lens: The case of china. International journal of environmental research and public health. 2020;17(7):2309.
95. Nekovee M, Moreno Y, Bianconi G, Marsili M. Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and its Applications. 2007;374(1):457-470.
96. Miritello G, Moro E, Lara R. Dynamical strength of social ties in information spreading. Physical Review E. 2011;83(4):045102.
97. Morales AJ, Borondo J, Losada JC, Benito RM. Efficiency of human activity on information spreading on Twitter. Social Networks. 2014;39:1-11.
98. Bodendorf F, Kaiser C. Detecting opinion leaders and trends in online social networks. 2009:65-68.
99. Pastor-Escuredo D, Tarazona C. Characterizing information leaders in Twitter during COVID-19 crisis. arXiv preprint arXiv:200507266. 2020;
100. Newman ME, Barabási A-LE, Watts DJ. The structure and dynamics of networks. Princeton university press; 2006.
101. Borge-Holthoefer J, Meloni S, Gonçalves B, Moreno Y. Emergence of influential spreaders in modified rumor models. Journal of Statistical Physics. 2013;151(1-2):383-393.
102. Popat K, Mukherjee S, Yates A, Weikum G. DeClarE: Debunking fake news and false claims using evidence-aware deep learning. arXiv preprint arXiv:180906416. 2018;
103. Ruchansky N, Seo S, Liu Y. Csi: A hybrid deep model for fake news detection. 2017:797-806.
104. Singhania S, Fernandez N, Rao S. 3han: A deep neural network for fake news detection. Springer; 2017:572-581.
105. Luengo-Oroz M, Pham KH, Bullock J, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nature Machine Intelligence. 2020:1-3.
106. Leung GM, Leung K. Crowdsourcing data to mitigate epidemics. The Lancet Digital Health. 2020;2(4):e156-e157.
107. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital Health. 2020;
108. Imran A, Posokhova I, Qureshi HN, et al. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked. 2020:100378.
109. Menni C, Valdes A, Freydin MB, et al. Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection. MedRxiv. 2020;
110. Carrillo-Larco RM, Castillo-Cara M. Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Research. 2020;5(56):56.
111. Hu Z, Ge Q, Jin L, Xiong M. Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:200207112. 2020;
112. Hartono P. Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder. arXiv preprint arXiv:200401481. 2020;
113. Liu D, Clemente L, Poirier C, et al. A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv preprint arXiv:200404019. 2020;
114. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology). 2005;67(2):301-320.
115. Bandyopadhyay SK, Dutta S. Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release. medRxiv. 2020;
116. Huang C-J, Chen Y-H, Ma Y, Kuo P-H. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. medRxiv. 2020;
117. Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. arXiv preprint arXiv:200310776. 2020;
118. Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature communications. 2019;10(1):1-10.
119. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences. 2015;112(47):14473-14478.
120. Rajarethinam J, Ong J, Lim S-H, et al. Using human movement data to identify potential areas of Zika transmission: case study of the largest Zika cluster in Singapore. International journal of environmental research and public health. 2019;16(5):808.
121. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance. 2020;25(10):2000180.
122. Moghadas SM, Fitzpatrick MC, Sah P, et al. The implications of silent transmission for the control of COVID-19 outbreaks. Proceedings of the National Academy of Sciences. 2020;117(30):17513-17515.
123. Kermali M, Khalsa RK, Pillai K, Ismail Z, Harky A. The role of biomarkers in diagnosis of COVID-19–A systematic review. Life Sciences. 2020:117788.
124. Shi Y, Wang Y, Shao C, et al. COVID-19 infection: the perspectives on immune responses. Nature Publishing Group; 2020.
125. Nicholas GD, Petra K, Yang L, et al. Age-dependent Effects in the Transmission and Control of COVID-19 Epidemics. Nature medicine. 2020;
126. Eames KT, Keeling MJ. Contact tracing and disease control. Proceedings of the Royal Society of London Series B: Biological Sciences. 2003;270(1533):2565-2571.
127. Dietz K. The estimation of the basic reproduction number for infectious diseases. Statistical methods in medical research. 1993;2(1):23-41.
128. Liu Q-H, Ajelli M, Aleta A, Merler S, Moreno Y, Vespignani A. Measurability of the epidemic reproduction number in data-driven contact networks. Proceedings of the National Academy of Sciences. 2018;115(50):12680-12685.
129. Glass RJ, Glass LM, Beyeler WE, Min HJ. Targeted social distancing designs for pandemic influenza. Emerging infectious diseases. 2006;12(11):1671.
130. Lampos V, Moura S, Yom-Tov E, Cox IJ, McKendry R, Edelstein M. Tracking COVID-19 using online search. arXiv preprint arXiv:200308086. 2020;
131. WHO. Infodemic management - Infodemiology;. WHO. 2020. https://www.who.int/teams/risk-communication/infodemic-management.
132. Singh L, Bansal S, Bode L, et al. A first look at COVID-19 information and misinformation sharing on Twitter. arXiv preprint arXiv:200313907. 2020;
133. Mejova Y, Kalimeri K. Advertisers jump on coronavirus bandwagon: Politics, news, and business. arXiv preprint arXiv:200300923. 2020;
134. Hu Z, Ge Q, Li S, Boerwincle E, Jin L, Xiong M. Forecasting and evaluating intervention of Covid-19 in the World. arXiv preprint arXiv:200309800. 2020;
135. Dandekar R, Barbastathis G. Neural Network aided quarantine control model estimation of global Covid-19 spread. arXiv preprint arXiv:200402752. 2020;
136. Lai S, Ruktanonchai NW, Zhou L, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. 2020;
137. Flaxman S, Mishra S, Gandy A, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020:1-5.
138. Davies NG, Kucharski AJ, Eggo RM, et al. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. The Lancet Public Health. 2020;
139. Cowling BJ, Ali ST, Ng TW, et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. The Lancet Public Health. 2020;
140. Taleb NN, Read R, Douady R, Norman J, Bar-Yam Y. The precautionary principle (with application to the genetic modification of organisms). arXiv preprint arXiv:14105787. 2014;
141. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438(7066):355-359.
142. Kupferschmidt K. Why do some COVID-19 patients infect many others, whereas most don’t spread the virus at all? Science. 19/05/2020 2020;
143. Mello MM, Wang CJ. Ethics and governance for digital disease surveillance. Science. 2020;
144. Kuhn C, Beck M, Strufe T. Covid Notions: Towards Formal Definitions--and Documented Understanding--of Privacy Goals and Claimed Protection in Proximity-Tracing Services. arXiv preprint arXiv:200407723. 2020;
145. Vinuesa R, Theodorou A, Battaglini M, Dignum V. A socio-technical framework for digital contact tracing. arXiv preprint arXiv:200508370. 2020;
146. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases. 2020;20(5):533-534.
147. Aleks Berditchevskaia KP. Coronavirus: seven ways collective intelligence is tackling the pandemic. The Conversation. 2020. 2020. https://theconversation.com/coronavirus-seven-ways-collective-intelligence-is-tackling-the-pandemic-133553
148. Pastor-Escuredo D, Tarazona-Lizarraga C, Bachmann A, Treleaven P. Collective Intelligence and Governance for Pandemics. Covid-19: A Complex Systems Approach. STEM Academic Press; 2021:223-248.
149. Pastor-Escuredo D. Ethics in the digital era. arXiv preprint arXiv:200306530. 2020;
150. Berman G, Carter K, Herranz MG, Sekara V. Digital contact tracing and surveillance during COVID-19. General and child-specific ethical issues. 2020.
151. General US. The age of digital interdependence. Report of the UN Secretary-General’s High-Level Panel on Digital Cooperation …; 2019.