COVID-19 has illuminated the need for clearer AI-based risk management strategies

References

• “IHME | COVID-19 Projections.” n.d. Institute for Health Metrics and Evaluation. Accessed June 29, 2020. https://covid19.healthdata.org/united-states-of-america.
   Google Scholar
• “Technical Guidance.” n.d. Accessed June 29, 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance.
   Google Scholar
• Abdulkareem, Musa, and Steffen E. Petersen. 2021. “The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.” Frontiers in Artificial Intelligence 4 (May): 652669. doi:10.3389/frai.2021.652669.
   PubMedGoogle Scholar
• Ackley, Calvin A., Dielle J. Lundberg, Lei Ma, Irma T. Elo, Samuel H. Preston, and Andrew C. Stokes. 2022. “County-Level Estimates of Excess Mortality Associated with COVID-19 in the United States.” SSM - Population Health 17 (March): 101021. doi:10.1016/j.ssmph.2021.101021.
   PubMedGoogle Scholar
• Adekola, Hafeez Aderinsayo, Ibrahim Ayoade Adekunle, Haneefat Olabimpe Egberongbe, Sefiu Adekunle Onitilo, and Idris Nasir Abdullahi. “Mathematical Modeling for Infectious Viral Disease: The COVID-19 Perspective.” Journal of Public Affairs 20 (4): e2306.
   PubMed Web of Science ®Google Scholar
• Alamo, Teodoro, Daniel G. Reina, Martina Mammarella, and Alberto Abella. 2020. “Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic.” Electronics 9 (5): 827. doi:10.3390/electronics9050827.
   Web of Science ®Google Scholar
• Amit, Moran, Heli Kimhi, Tarif Bader, Jacob Chen, Elon Glassberg, and Avi Benov. 2020. “Mass-Surveillance Technologies to Fight Coronavirus Spread: The Case of Israel.” Nature Medicine 26 (8): 1167–1169. doi:10.1038/s41591-020-0927-z.
   PubMed Web of Science ®Google Scholar
• Aven, Terje, Yakov Ben-Haim, H. Boje Andersen, Tony Cox, Enrique López Droguett, Michael Greenberg, Seth Guikema, et al. 2018. “Society for Risk Analysis Glossary.” https://www.sra.org/wp-content/uploads/2020/04/SRA-Glossary-FINAL.pdf.
   Google Scholar
• Aven, Terje. 2011. “On Some Recent Definitions and Analysis Frameworks for Risk, Vulnerability, and Resilience.” Risk Analysis: An Official Publication of the Society for Risk Analysis 31 (4): 515–522. doi:10.1111/j.1539-6924.2010.01528.x.
   PubMed Web of Science ®Google Scholar
• Aven, Terje, and Roger Flage. 2020. “Foundational Challenges for Advancing the Field and Discipline of Risk Analysis.” Risk Analysis 40 (S1): 2128–2136. doi:10.1111/risa.13496.
   PubMedGoogle Scholar
• Banks, David, Laura Albert, Jonathan Caulkins, Sylvia Frühwirth-Schnatter, Fiona Greig, Adrian Raftery, and Duncan Thomas. 2020. “A Conversation about COVID-19 with Economists, Sociologists, Statisticians, and Operations Researchers.” Harvard Data Science Review . doi:10.1162/99608f92.7fa08812.
   Google Scholar
• Barry, Ellen. 2020. “‘It’s Totally Ad Hoc’: Why America’s Virus Response Looks Like a Patchwork.” The New York Times, March 15, 2020. https://www.nytimes.com/2020/03/15/us/united-states-coronavirus-response.html.
   Google Scholar
• Biswas, Kathakali, and Parongama Sen. 2020. “Space-Time Dependence of Corona Virus (COVID-19) Outbreak.” ArXiv [Physics.Soc-Ph]. arXiv. http://arxiv.org/abs/2003.03149.
   Google Scholar
• Bjerge, Benedikte, Nathan Clark, Peter Fisker, and Emmanuel Raju. 2016. “Technology and Information Sharing in Disaster Relief.” PloS One 11 (9): e0161783. doi:10.1371/journal.pone.0161783.
   PubMed Web of Science ®Google Scholar
• Broen, Kelly, Rob Trangucci, and Jon Zelner. 2021. “Measuring the Impact of Spatial Perturbations on the Relationship between Data Privacy and Validity of Descriptive Statistics.” International Journal of Health Geographics 20 (1): 3. doi:10.1186/s12942-020-00256-8.
   PubMed Web of Science ®Google Scholar
• Bump, Philip. 2020. “The New Political Battleground over the Coronavirus? Math.” The Washington Post, May 19, 2020. https://www.washingtonpost.com/politics/2020/05/19/new-political-battleground-over-coronavirus-math/.
   Google Scholar
• Callaghan, Timothy, Jennifer A. Lueck, Kristin Lunz Trujillo, and Alva O. Ferdinand. 2021. “Rural and Urban Differences in COVID-19 Prevention Behaviors.” The Journal of Rural Health: official Journal of the American Rural Health Association and the National Rural Health Care Association 37 (2): 287–295. doi:10.1111/jrh.12556.
   PubMed Web of Science ®Google Scholar
• Canakoglu, Arif, Pietro Pinoli, Andrea Gulino, Luca Nanni, Marco Masseroli, and Stefano Ceri. 2021. “Federated Sharing and Processing of Genomic Datasets for Tertiary Data Analysis.” Briefings in Bioinformatics 22 (3): bbaa091.
   PubMed Web of Science ®Google Scholar
• CDC. 2020. “Coronavirus Disease 2019 (COVID-19).” Centers for Disease Control and Prevention. June 27, 2020. https://www.cdc.gov/coronavirus/2019-ncov/communication/guidance-list.html?Sort=Date%3A%3Adesc.
   Google Scholar
• Chamola, Vinay, Vikas Hassija, Vatsal Gupta, and Mohsen Guizani. 2020. “A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing Its Impact.” IEEE Access 8: 90225–90265. doi:10.1109/ACCESS.2020.2992341.
   Web of Science ®Google Scholar
• Chimmula, Vinay Kumar Reddy, and Lei Zhang. 2020. “Time Series Forecasting of COVID-19 Transmission in Canada Using LSTM Networks.” Chaos, Solitons, and Fractals 135: 109864. doi:10.1016/j.chaos.2020.109864.
   PubMed Web of Science ®Google Scholar
• “Coronavirus Resource Center.” n.d. Johns Hopkins University. Accessed July 15, 2020. https://coronavirus.jhu.edu/.
   Google Scholar
• “COVID-19 Government Response Tracker.” n.d. Accessed January 21, 2022. https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker.
   Google Scholar
• Desmet, Klaus, and Romain Wacziarg. 2022. “JUE Insight: Understanding Spatial Variation in COVID-19 Across the United States.” Journal of Urban Economics 127 (2022): 103332.
   PubMedGoogle Scholar
• deSouza, Priyanka N., and S. V. Subramanian. 2020. “COVID-19 Across United States Congressional Districts.” Journal of Global Health Science 2 (2).
   Google Scholar
• DiMaggi, Charles, Michael Klein, Cherisse Berry, and Spiros Frangos. 2020. “Blacks/African Americans are 5 Times More Likely to Develop COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results.” Epidemiology 51 (2020): 7–13..
   Google Scholar
• Dubrow, Joshua K. 2021. “Local Data and Upstream Reporting as Sources of Error in the Administrative Data Undercount of Covid 19.” International Journal of Social Research Methodology: 1–6. doi:10.1080/13645579.2021.1909337.
   Web of Science ®Google Scholar
• Dutta, Shawni, and Samir Kumar Bandyopadhyay. 2020. “Machine Learning Approach for Confirmation of Covid-19 Cases: Positive, Negative, Death and Release..” Iberoamerican Journal of Medicine 2 (3): 172–177.
   Google Scholar
• Elliott, Diana, Robert Santos, Steven Martin, and Charmaine Runes. 2019. “Assessing Miscounts in the 2020 Census.” Urban Institute. June 3, 2019. https://www.urban.org/research/publication/assessing-miscounts-2020-census.
   Google Scholar
• Fan, Ying., A. Yeşim Orhun, and Dana Turjeman. 2020. Heterogeneous Actions, Beliefs, Constraints and Risk Tolerance During the COVID-19 Pandemic. No. w27211. National Bureau of Economic Research.
   Google Scholar
• Fenton, Norman E., Martin Neil, Magda Osman, and Scott McLachlan. 2020. “COVID-19 Infection and Death Rates: The Need to Incorporate Causal Explanations for the Data and Avoid Bias in Testing.” Journal of Risk Research 23 (7–8): 862–865. doi:10.1080/13669877.2020.1756381.
   Web of Science ®Google Scholar
• Fung, Benjamin, C. M. Ke Wang, Rui Chen, and Philip S. Yu. 2010. “Privacy-Preserving Data Publishing: A Survey of Recent Developments.” ACM Computing Surveys 14 (4): 1–53.
   Google Scholar
• Galea, Sandro. 2013. “An Argument for a Consequentialist Epidemiology.” American Journal of Epidemiology 178 (8): 1185–1191. doi:10.1093/aje/kwt172.
   PubMed Web of Science ®Google Scholar
• Gardner, Lauren, Jeremy Ratcliff, Ensheng Dong, and Aaron Katz. 2021. “A Need for Open Public Data Standards and Sharing in Light of COVID-19.” The Lancet. Infectious Diseases 21 (4): e80. doi:10.1016/S1473-3099(20)30635-6.
   PubMed Web of Science ®Google Scholar
• Gostin, Lawrence O., and Sharyl Nass. 2009. “Reforming the HIPAA Privacy Rule: Safeguarding Privacy and Promoting Research.” JAMA 301 (13): 1373–75. doi:10.1001/jama.2009.424.
   PubMed Web of Science ®Google Scholar
• Gu, Youyang. n.d. “COVID-19 Projections Using Machine Learning.” Accessed June 29, 2020. https://covid19-projections.com/about/.
   Google Scholar
• Guikema, Seth. 2020. “Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs.” Risk Analysis: An Official Publication of the Society for Risk Analysis 40 (6): 1117–1123. doi:10.1111/risa.13476.
   PubMed Web of Science ®Google Scholar
• Haendel, Melissa A., Christopher G. Chute, Tellen D. Bennett, David A. Eichmann, Justin Guinney, Warren A. Kibbe, Philip R. O. Payne, N3C Consortium, et al. 2021. “The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment.” Journal of the American Medical Informatics Association: JAMIA 28 (3): 427–443. doi:10.1093/jamia/ocaa196.
   PubMed Web of Science ®Google Scholar
• Hamidi, Shima, Sadegh Sabouri, and Reid Ewing. 2020. “Does Density Aggravate the COVID-19 Pandemic? Early Findings and Lessons for Planners.” Journal of the American Planning Association 86 (4): 495–509.
   Web of Science ®Google Scholar
• Hao, Karen. 2020. “This Is How the CDC Is Trying to Forecast Coronavirus’s Spread.” MIT Technology Review, March 13, 2020. https://www.technologyreview.com/2020/03/13/905313/cdc-cmu-forecasts-coronavirus-spread/.
   Google Scholar
• Holcombe, Madeline. 2020. “More than Half of States May Be Undercounting Coronavirus Cases by Not Following CDC Guidelines.” CNN, June 9, 2020. https://www.cnn.com/2020/06/09/health/us-coronavirus-tuesday/index.html.
   Google Scholar
• Hu, Hao, Karima Nigmatulina, and Philip Eckhoff. 2013. “The Scaling of Contact Rates with Population Density for the Infectious Disease Models.” Mathematical Biosciences 244 (2): 125–134. doi:10.1016/j.mbs.2013.04.013.
   PubMed Web of Science ®Google Scholar
• Ienca, Marcello, and Effy Vayena. 2020. “On the Responsible Use of Digital Data to Tackle the COVID-19 Pandemic.” Nature Medicine 26 (4): 463–464. doi:10.1038/s41591-020-0832-5.
   PubMed Web of Science ®Google Scholar
• Ioannidis, John P. A., Sally Cripps, and Martin A. Tanner. 2022. “Forecasting for COVID-19 Has Failed.” International Journal of Forecasting 38 (2): 423–438. doi:10.1016/j.ijforecast.2020.08.004.
   PubMed Web of Science ®Google Scholar
• Jit, Mark, Aparna Ananthakrishnan, Martin McKee, Olivier J. Wouters, Philippe Beutels, and Yot Teerawattananon. 2021. “Multi-Country Collaboration in Responding to Global Infectious Disease Threats: Lessons for Europe from the COVID-19 Pandemic.” the Lancet Regional Health.” The Lancet Regional Health. Europe 9 (October): 100221. doi:10.1016/j.lanepe.2021.100221.
   PubMedGoogle Scholar
• Kaplan, Stanley, and B. John Garrick. 1981. “On the Quantitative Definition of Risk.” Risk Analysis: An Official Publication of the Society for Risk Analysis 1 (1): 11–27. doi:10.1111/j.1539-6924.1981.tb01350.x.
   Google Scholar
• Katz, Josh, Denise Lu, and Margot Sanger-Katz. 2020. “What Is the Real Coronavirus Death Toll in Each State?” The New York Times, May 6, 2020. https://www.nytimes.com/interactive/2020/05/05/us/coronavirus-death-toll-us.html.
   Google Scholar
• Keeling, Matt J., and Pejman Rohani. 2011. “Modeling Infectious Diseases in Humans and Animals..” In Modeling Infectious Diseases in Humans and Animals. Princeton, New Jersey, USA: Princeton University Press.
   Google Scholar
• Kermack, William Ogilvy, A. G. McKendrick, and Gilbert Thomas Walker. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115 (772): 700–721.
   Google Scholar
• Khan, Mishal S., Osman Dar, Ngozi A. Erondu, Afifah Rahman-Shepherd, Lara Hollmann, Chikwe Ihekweazu, Okechukwu Ukandu, et al. 2020. “Using Critical Information to Strengthen Pandemic Preparedness: The Role of National Public Health Agencies.” BMJ Global Health 5 (9): e002830. doi:10.1136/bmjgh-2020-002830.
   PubMed Web of Science ®Google Scholar
• Koerth, Maggie, Laura Bronner, and Jasmine Mithani. 2020. “Why It’s So Freaking Hard To Make A Good COVID-19 Model.” FiveThirtyEight. March 31, 2020. https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/.
   Google Scholar
• Krumholz, Harlan M. 2020. “Where Have All the Heart Attacks Gone?” The New York Times, April 6, 2020. https://www.nytimes.com/2020/04/06/well/live/coronavirus-doctors-hospitals-emergency-care-heart-attack-stroke.html.
   Google Scholar
• Laurencin, Cato T., and Aneesah McClinton. 2020. “The COVID-19 Pandemic: A Call to Action to Identify and Address Racial and Ethnic Disparities.” Journal of Racial and Ethnic Health Disparities 7 (3): 398–402. doi:10.1007/s40615-020-00756-0.
   PubMed Web of Science ®Google Scholar
• Leon, David A., Vladimir M. Shkolnikov, Liam Smeeth, Per Magnus, Markéta Pechholdová, and Christopher I. Jarvis. 2020. “COVID-19: A Need for Real-Time Monitoring of Weekly Excess Deaths.” The Lancet 395 (10234): e81. doi:10.1016/S0140-6736(20)30933-8.
   PubMed Web of Science ®Google Scholar
• Li, Lin, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, et al. 2020. “Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT.” Radiology: 200905.
   PubMedGoogle Scholar
• Lloyd-Smith, J. O., S. J. Schreiber, P. E. Kopp, and W. M. Getz. 2005. “Superspreading and the Effect of Individual Variation on Disease Emergence.” Nature 438 (7066): 355–59. doi:10.1038/nature04153.
   PubMed Web of Science ®Google Scholar
• Logan, T. M., S. D. Guikema, and J. D. Bricker. 2018. “Hard-Adaptive Measures Can Increase Vulnerability to Storm Surge and Tsunami Hazards over Time.” Nature Sustainability 1 (9): 526–530. doi:10.1038/s41893-018-0137-6.
   Web of Science ®Google Scholar
• Lu, Fred S., Andre T. Nguyen, Nicholas B. Link, Mathieu Molina, Jessica T. Davis, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Marc Lipsitch, and Mauricio Santillana. 2021. “Estimating the Cumulative Incidence of COVID-19 in the United States Using Influenza Surveillance, Virologic Testing, and Mortality Data: Four Complementary Approaches.” PLoS Computational Biology 17 (6): e1008994.
   PubMed Web of Science ®Google Scholar
• Ma, Ruifang, Xinqi Zheng, Peipei Wang, Haiyan Liu, and Chunxiao Zhang. 2021. “The Prediction and Analysis of COVID-19 Epidemic Trend by Combining LSTM and Markov Method.” Scientific Reports 11 (1): 17421. doi:10.1038/s41598-021-97037-5.
   PubMed Web of Science ®Google Scholar
• Maher, M. Cyrus, Istvan Bartha, Steven Weaver, Julia di Iulio, Elena Ferri, Leah Soriaga, Florian A. Lempp, et al. 2022. “Predicting the Mutational Drivers of Future SARS-CoV-2 Variants of Concern.” Science Translational Medicine 14 (633): eabk3445. https://www.science.org/doi/full/101126/scitranslmed.abk3445. doi:10.1126/scitranslmed.abk3445.
   PubMed Web of Science ®Google Scholar
• Martín, Yago, Susan L. Cutter, and Zhenlong Li. 2020. “Bridging Twitter and Survey Data for Evacuation Assessment of Hurricane Matthew and Hurricane Irma.” Natural Hazards Review 21 (2): 04020003. doi:10.1061/(ASCE)NH.1527-6996.0000354.
   Web of Science ®Google Scholar
• Maxmen, Amy. 2020. “Why the United States is Having a Coronavirus Data Crisis.” Nature 585 (7823): 13–14. doi:10.1038/d41586-020-02478-z.
   PubMed Web of Science ®Google Scholar
• Mei, Xueyan, Hao-Chih Lee, Kai-Yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie, et al. 2020. “Artificial Intelligence-Enabled Rapid Diagnosis of Patients with COVID-19.” Nature Medicine 26 (8): 1224–1228. doi:10.1038/s41591-020-0931-3.
   PubMed Web of Science ®Google Scholar
• Pentland, Alex, and Thomas Hardjono. 2020. “10. Towards an Ecosystem of Trusted Data and AI.” In Building the New Economy Pub. doi:10.21428/ba67f642.0499afe0.
   Google Scholar
• Prasso, Sheridan. 2020. “Coronavirus Surveillance Helps, But the Programs Are Hard to Stop.” Bloomberg News, April 6, 2020. https://www.bloomberg.com/news/articles/2020-04-06/coronavirus-surveillance-helps-but-the-programs-are-hard-to-stop.
   Google Scholar
• Reisen, Mirjam van, Francisca Oladipo, Mia Stokmans, Mouhamed Mpezamihgo, Sakinat Folorunso, Erik Schultes, Mariam Basajja, et al. 2021. “Design of a FAIR Digital Data Health Infrastructure in Africa for COVID-19 Reporting and Research.” Advances in Genetics 2 (2): e10050.
   PubMedGoogle Scholar
• Rocklöv, Joacim, and Henrik Sjödin. 2020. “High Population Densities Catalyse the Spread of COVID-19.” Journal of Travel Medicine 27 (3): taaa038.
   PubMed Web of Science ®Google Scholar
• Schüssler-Fiorenza Rose, Sophia Miryam, Kévin Contrepois, Kegan J. Moneghetti, Wenyu Zhou, Tejaswini Mishra, Samson Mataraso, Orit Dagan-Rosenfeld, et al. 2019. “A Longitudinal Big Data Approach for Precision Health.” Nature Medicine 25 (5): 792–804. doi:10.1038/s41591-019-0414-6.
   PubMed Web of Science ®Google Scholar
• Shaman, Jeffrey, and Alicia Karspeck. 2012. “Forecasting Seasonal Outbreaks of Influenza.” Proceedings of the National Academy of Sciences of the United States of America 109 (50): 20425–20430. doi:10.1073/pnas.1208772109.
   PubMed Web of Science ®Google Scholar
• Sipior, Janice C. 2020. “Considerations for Development and Use of AI in Response to COVID-19.” International Journal of Information Management 55 (December): 102170. doi:10.1016/j.ijinfomgt.2020.102170.
   PubMedGoogle Scholar
• Slovic, P. 1987. “Perception of Risk.” Science (New York, NY) 236 (4799): 280–85. doi:10.1126/science.3563507.
   Google Scholar
• Soures, Nicholas, David Chambers, Zachariah Carmichael, Anurag Daram, Dimpy P. Shah, Kal Clark, Lloyd Potter, and Dhireesha Kudithipudi. 2020. “SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread.” arXiv preprint arXiv: 2004. 10376.
   Google Scholar
• Sport, Guardian. 2020. “Just 0.7% of MLB Employees Test Positive for Covid-19 Antibodies.” The Guardian, May 11, 2020. http://www.theguardian.com/sport/2020/may/11/mlb-baseball-covid-19-coronavirus-antibodies-test.
   Google Scholar
• Srinivasa, Arni S., and Jose A. Vazquez. 2020. “Identification of COVID-19 Can Be Quicker through Artificial Intelligence Framework Using a Mobile Phone-Based Survey When Cities and Towns Are Under Quarantine.” Infection Control and Hospital Epidemiology 41 (7): 826–830. doi:10.1017/ice.2020.61.
   PubMed Web of Science ®Google Scholar
• Stewart, Geoffrey T., Ramesh Kolluru, and Mark Smith. 2009. “Leveraging Public‐Private Partnerships to Improve Community Resilience in Times of Disaster.” International Journal of Physical Distribution & Logistics Management 39 (5): 343–364. doi:10.1108/09600030910973724.
   Web of Science ®Google Scholar
• Strane, Douglas, and Heather M. Griffis. 2018. “Inaccuracies in the 2020 Census Enumeration Could Create a Misalignment between States’ Needs.” American Journal of Public Health 108 (10): 1330–1333. doi:10.2105/AJPH.2018.304569.
   PubMed Web of Science ®Google Scholar
• Tahir, Darius, and Adam Cancryn. 2020. “Bad State Data Hides Coronavirus Threat as Trump Pushes Reopening.” POLITICO. Accessed June 29, 2020. https://www.politico.com/news/2020/05/27/bad-state-coronavirus-data-trump-reopening-286143.
   Google Scholar
• Tam Chor-Cheung, Frankie, Cheung Kent-Shek, Lam Simon, Wong Anthony, Yung Arthur, Sze Michael, Lam Yui-Ming, et al. 2020. “Impact of Coronavirus Disease 2019 (COVID-19) Outbreak on ST-Segment–Elevation Myocardial Infarction Care in Hong Kong, China.” Circulation. Cardiovascular Quality and Outcomes 13 (4): e006631.
   PubMed Web of Science ®Google Scholar
• Tate, Eric. 2012. “Social Vulnerability Indices: A Comparative Assessment Using Uncertainty and Sensitivity Analysis.” Natural Hazards 63 (2): 325–347. doi:10.1007/s11069-012-0152-2.
   Web of Science ®Google Scholar
• The New York Times. 2020. “We’re Sharing Coronavirus Case Data for Every U.S. County.” The New York Times, March 30, 2020. https://www.nytimes.com/article/coronavirus-county-data-us.html.
   Google Scholar
• Theodorou, Andreas, and Virginia Dignum. 2020. “Towards Ethical and Socio-Legal Governance in AI.” Nature Machine Intelligence 2 (1): 10–12. doi:10.1038/s42256-019-0136-y.
   Google Scholar
• This story was written by BUMED Public Affairs. n.d. “Findings From USS Theodore Roosevelt Public Health Investigation Support Force Health Protection.” Accessed June 29, 2020. https://www.navy.mil/submit/display.asp?story_id=113212.
   Google Scholar
• Thomas, Loring J., Peng Huang, Fan Yin, Xiaoshuang Iris Luo, Zack W. Almquist, John R. Hipp, and Carter T. Butts. 2020. “Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity.” Proceedings of the National Academy of Sciences of the United States of America 117 (39): 24180–24187. doi:10.1073/pnas.2011656117.
   PubMed Web of Science ®Google Scholar
• Tripepi, G., K. J. Jager, F. W. Dekker, C. Wanner, and C. Zoccali. 2008. “Bias in Clinical Research.” Kidney International 73 (2): 148–153. doi:10.1038/sj.ki.5002648.
   PubMed Web of Science ®Google Scholar
• Tzachor, Asaf, Jess Whittlestone, Lalitha Sundaram, and Seán Ó. hÉigeartaigh. 2020. “Artificial Intelligence in a Crisis Needs Ethics with Urgency.” Nature Machine Intelligence 2 (7): 365–366. doi:10.1038/s42256-020-0195-0.
   Google Scholar
• Vaishya, Raju, Mohd Javaid, Ibrahim Haleem Khan, and Abid Haleem. 2020. “Artificial Intelligence (AI) Applications for COVID-19 Pandemic.” Diabetes & Metabolic Syndrome 14 (4): 337–339. doi:10.1016/j.dsx.2020.04.012.
   PubMed Web of Science ®Google Scholar
• Vasudevan, Varun, Abeynaya Gnanasekaran, Varsha Sankar, Siddarth A. Vasudevan, and James Zou. 2021. “Disparity in the Quality of COVID-19 Data Reporting across India.” BMC Public Health 21 (1): 1211. doi:10.1186/s12889-021-11054-7.
   PubMed Web of Science ®Google Scholar
• Vayena, E. 2021. “Value from Health Data: European Opportunity to Catalyse Progress in Digital Health.” The Lancet 397 (10275): 652–653. doi:10.1016/S0140-6736(21)00203-8.
   PubMed Web of Science ®Google Scholar
• Verity, Robert, Lucy C. Okell, Ilaria Dorigatti, Peter Winskill, Charles Whittaker, Natsuko Imai, Gina Cuomo-Dannenburg, et al. 2020. “Estimates of the Severity of Coronavirus Disease 2019: A Model-Based Analysis.” The Lancet Infectious Diseases 20 (6): 669–677. doi:10.1016/S1473-3099(20)30243-7.
   PubMed Web of Science ®Google Scholar
• Wesolowski, Amy, Caroline O. Buckee, Kenth Engø-Monsen, and C. J. E. Metcalf. 2016. “Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data.” The Journal of Infectious Diseases 214 (suppl_4): S414–S20. doi:10.1093/infdis/jiw273.
   PubMedGoogle Scholar
• White, James M., and Rolf Lidskog. 2021. “Ignorance and the Regulation of Artificial Intelligence.” Journal of Risk Research 25 (2): 488–500.
   Google Scholar
• Wu, Xiao, Rachel C. Nethery, M. Benjamin Sabath, Danielle Braun, and Francesca Dominici. 2020. “Exposure to Air Pollution and COVID-19 Mortality in the United States: A Nationwide Cross-Sectional Study.”
   Google Scholar
• Wynants, Laure, Ben Van Calster, Gary S. Collins, Richard D. Riley, Georg Heinze, Ewoud Schuit, Marc M. J. Bonten, et al. 2020. “Prediction Models for Diagnosis and Prognosis of Covid-19: Systematic Review and Critical Appraisal.” BMJ (Clinical Research ed.) 369 (April): m1328. doi:10.1136/bmj.m1328.
   PubMedGoogle Scholar
• Xia, Yingcun, Ottar N. Bjørnstad, and Bryan T. Grenfell. 2004. “Measles Metapopulation Dynamics: A Gravity Model for Epidemiological Coupling and Dynamics.” The American Naturalist 164 (2): 267–281. doi:10.1086/422341.
   PubMed Web of Science ®Google Scholar
• Yan, Bingjie, Xiangyan Tang, Boyi Liu, Jun Wang, Yize Zhou, Guopeng Zheng, Qi Zou, Yao Lu, and Wenxuan Tu. 2020. “An Improved Method for the Fitting and Prediction of the Number of Covid-19 Confirmed Cases Based on LSTM.” arXiv preprint arXiv:2005.03446.
   Google Scholar
• Yan, Li, Hai-Tao Zhang, Jorge Goncalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, et al. 2020. “An Interpretable Mortality Prediction Model for COVID-19 Patients.” Nature Machine Intelligence 2 (5): 283–288. doi:10.1038/s42256-020-0180-7.
   Google Scholar
• Yudistira, Novanto, Sutiman Bambang Sumitro, Alberth Nahas, and Nelly Florida Riama. 2021. “Learning Where to Look for COVID-19 Growth: Multivariate Analysis of COVID-19 Cases Over Time Using Explainable Convolution–LSTM.” Applied Soft Computing 109 (2021): 107469.
   PubMedGoogle Scholar
• Zandavi, Seid Miad, Taha Hossein Rashidi, and Fatemeh Vafaee. 2021. “Dynamic Hybrid Model to Forecast the Spread of COVID-19 Using LSTM and Behavioral Models Under Uncertainty.” IEEE Transactions on Cybernetics.
   PubMed Web of Science ®Google Scholar
• Zelner, Jon, Julien Riou, Ruth Etzioni, and Andrew Gelman. 2021. “Accounting for Uncertainty during a Pandemic.” Patterns (New York, NY) 2 (8): 100310.
   PubMedGoogle Scholar
• Zelner, Jon, Nina B. Masters, Ramya Naraharisetti, Sanyu A. Mojola, Merlin Chowkwanyun, and Ryan Malosh. 2022. “There Are No Equal Opportunity Infectors: Epidemiological Modelers Must Rethink Our Approach to Inequality in Infection Risk.” PLoS Computational Biology 18 (2): e1009795. doi:10.1371/journal.pcbi.1009795.
   PubMed Web of Science ®Google Scholar
• Zhao, Shi, Qianyin Lin, Jinjun Ran, Salihu S. Musa, Guangpu Yang, Weiming Wang, Yijun Lou, et al. 2020. “Preliminary Estimation of the Basic Reproduction Number of Novel Coronavirus (2019-NCoV) in China, from 2019 to 2020: A Data-Driven Analysis in the Early Phase of the Outbreak.” International Journal of Infectious Diseases 92 (March): 214–217. doi:10.1016/j.ijid.2020.01.050.
   PubMedGoogle Scholar