Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches
Open accessPreprint — published as 10.1371/journal.pcbi.1008994
Abstract
Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.
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Estimates of I maximizing Eq. 1 obtained with an expectation maximization algorithm are typically unstable.
“Estimates of I maximizing Eq. 1 can be obtained with an expectation maximization algorithm (6, 33–35), but the outcome is typically unstable (36).”
From Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (2021) · cites this paper as [6]
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Wu, F.-Q., Xiao, A., Zhang, J.-B., Moniz, K., Endo, N., Armas, F., Bonneau, R., Brown, M. A., Bushman, M., Chai, P., et al. (2020) · medRxiv · doi:10.1101/2020.06.15.20117747 · Cited by 111
Self-citationSetting the criteria for SARS-CoV-2 reinfection – six possible cases
Kroemer, M., Spehner, L., Vettoretti, L., Bouard, A., Eberst, G., Pili-floury, S., Capellier, G., Lepiller, Q., Orillard, E., Mansi, L., et al. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.08.011 · Cited by 104 · see citing works
Predictive performance of international COVID-19 mortality forecasting modelsPreprint
Friedman, J., Liu, P. Y., Troeger, C. E., Carter, A., Reiner, R. C., Barber, R. M., Collins, J., Lim, S. S., Pigott, D., Vos, T., et al. (2020) · medRxiv · doi:10.1101/2020.07.13.20151233 · Cited by 82 · see citing works
A novel bio-system reliability approach for multi-state COVID-19 epidemic forecast
Gaidai, O., & Yihan, Y. (2022) · Engineering Sciences · doi:10.30919/es8d797 · Cited by 42
Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic
Tuncer, N., Timsina, A., Nuño, M., Chowell, G., & Martcheva, M. (2022) · Journal of Biological Dynamics · doi:10.1080/17513758.2022.2078899 · Cited by 34
Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data
Quick, C., Dey, R., & Lin, X. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2001339 · Cited by 28 · see citing works
A holistic approach for suppression of COVID-19 spread in workplaces and universities
Poole, S. F., Gronsbell, J., Winter, D., Nickels, S., Levy, R., Fu, B.-H., Burq, M., Saeb, S., Edwards, M. D., Behr, M., et al. (2020) · medRxiv · doi:10.1371/journal.pone.0254798 · Cited by 25 · see citing works
Padalabalanarayanan, S., Hanumanthu, V. S., & Sen, B. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.26010 · Cited by 25 · see citing works
Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy
Quaranta, G., Formica, G., Machado, J. A. T., Lacarbonara, W., & Masri, S. (2020) · Nonlinear dynamics · doi:10.1007/s11071-020-05902-1 · Cited by 23
Estimation of Covid-19 prevalence from serology tests: A partial identification approach
Toulis, P. (2020) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.005 · Cited by 23
“2.5% USA 04/04 Model Uses ILINet data; implies 96% unreported cases (Lu et al., 2020).”
BackgroundInfluentialHigh-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S
Zhu, S., Bukharin, A., Xie, L.-Y., Santillana, M., Yang, S., & Xie, Y. (2020) · ACM Transactions on Management Information Systems · doi:10.1145/3468876 · Cited by 20 · see citing works
“For example, when there was not enough testing capacity, as was the case in New York City in March 2020, the number of cases reported was clearly an undercount of true cases, estimated to be much larger (up by a factor of 10) [14, 24].”
BackgroundSelf-citationMcAndrew, T., Codi, A., Cambeiro, J., Besiroglu, T., Braun, D., Chen, E., Césaris, L. E. U. D., & Luk, D. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07794-5 · Cited by 7 · see citing works
Estimating the infection burden of COVID-19 in Malaysia
Jayaraj, V. J., Ng, C., Bulgiba, A., Appannan, M. R., & Rampal, S. (2022) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0010887 · Cited by 13 · see citing works
Douglas, J., Mendes, F., Bouckaert, R., Xie, D.-W., Jiménez-Silva, C., Swanepoel, C., de Ligt, J., Ren, X.-Y., Storey, M., Hadfield, J., et al. (2020) · medRxiv · doi:10.1101/2020.08.04.20168518 · Cited by 12
“Because COVID-19 often causes mild symptoms (Day, 2020b,a), the difference between the true and reported number of cases could be of an order of magnitude (Li et al., 2020a; Lu et al., 2020).”
BackgroundBhanot, G., & DeLisi, C. (2020) · Research Square · doi:10.21203/rs.3.rs-97697/v1 · Cited by 11
“It is well known that confirmed infected cases seriously underestimate the actual number of infections [16,17]: not everyone who is infected is symptomatic, and not everyone who dies from the disease has been tested [18].”
BackgroundSuperspreading of SARS-CoV-2 in the USA
Pozderac, C., & Skinner, B. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248808 · Cited by 10 · see citing works
Xu, H., Berres, A., Thakur, G. S., Sanyal, J., & Chinthavali, S. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103941 · Cited by 9 · see citing works
An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
McAndrew, T., & Reich, N. (2020) · medRxiv · doi:10.1101/2020.09.21.20196725 · Cited by 9
SARS-CoV-2 infections in 165 countries over time
Louca, S. (2021) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2021.08.067 · Cited by 7 · see citing works
Characterization of partially observed epidemics through Bayesian inference: application to COVID-19
Safta, C., Ray, J., & Sargsyan, K. (2020) · Computational Mechanics · doi:10.1007/s00466-020-01897-z · Cited by 7
Identity and COVID-19 in Canada: Gender, ethnicity, and minority status
Pongou, R., Ahinkorah, B., Mabeu, M. C., Agarwal, A., Maltais, S., Moumouni, A. B., & Yaya, S. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001156 · Cited by 6 · see citing works
Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method
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“Extending the GDM framework in Stoner & Economou (2020) to include a spatial dimension s ∈ S (e.”
Five approaches to the suppression of SARS-CoV-2 without intensive social distancing
Drake, J. J., Dahlin, K., Rohani, P., & Handel, A. (2021) · Proceedings of the Royal Society B · doi:10.1098/rspb.2020.3074 · Cited by 5 · see citing works
Sichone, J., Sinkala, M., Kikonko, M., Munsaka, S. M., & Simuunza, M. C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249479 · Cited by 4 · see citing works
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Experimental Technologies in the Diagnosis and Treatment of COVID-19 in Patients with Comorbidities
Amin, M. S., Woźniak, M., Barbaric, L., Pickard, S. C., Yerrabelli, R., Christensen, A., & Coiado, O. (2021) · J. Heal. Informatics Res. · doi:10.1007/s41666-021-00106-7 · Cited by 3 · see citing works
SARS-CoV-2 infections in 171 countries and over timePreprint
Louca, S. (2020) · medRxiv · doi:10.1101/2020.12.01.20241539 · Cited by 2 · see citing works
“In contrast to case reports, COVID-19-related death counts are generally regarded as less sensitive to testing effort and strategy [5, 6], and fortunately most countries have established nationwide continuous reporting mechanisms for death counts.”
BackgroundResultEffect of preventive measures in the containment of SARS-CoV-2 epidemic: a comparative study
Hoguane, A., Priya, K., Haddout, S., & Ljubenkov, I. (2021) · Health Care for Women International · doi:10.1080/07399332.2021.1876066 · Cited by 2
“The reliability of the data depends on the country’s ability to track infected people, collect and analyze the samples and record the death as argued by (Ibrahim, 2020; Lu et al., 2020).”
BackgroundAsymmetry in the peak in Covid-19 daily cases and the pandemic R-parameter
Bhatkar, S., Ma, M., Zsolway, M., Tarafder, A., Doniach, S., & Bhanot, G. (2023) · medRxiv · doi:10.1101/2023.07.23.23292960 · Cited by 1
“At any given moment in time, the reported number of infected individuals is a subset of the actual number of infected individuals [16,17].”
BackgroundSchulman, A., & Bhanot, G. (2021) · medRxiv · doi:10.1101/2021.03.29.21254583 · Cited by 1
Díaz-Castrillón, C. E., Cortés, N., Díaz-Castrillón, J. F., Pineda, M., & Tobón, S. S. (2021) · doi:10.30944/20117582.877 · Cited by 1
Ma, M., Zsolway, M., Tarafder, A., & Bhanot, G. (2022) · medRxiv · doi:10.1101/2022.09.05.22279599
“It is well known that in most pandemics, confirmed infected cases often seriously underestimate the actual number of infections [16,17]: not everyone who is infected is symptomatic, and not everyone who dies from the disease has been tested [18].”
BackgroundFive approaches to the suppression of SARS-CoV-2 without intensive social distancing
Drake, J., Rohani, P., Dahlin, K., & Handel, A. (2020) · medRxiv · doi:10.1101/2020.07.30.20165159
Disentangling Increased Testing From Covid-19 Epidemic SpreadPreprint
Lengerich, B. J., Neiswanger, W., Lengerich, E. J., & Xing, E. (2020) · medRxiv · doi:10.1101/2020.07.09.20141762
“By using the pre-existing ILI detection systems to estimate the increase in ILI disease prevalence, [5] obtain estimates of the SARS-CoV-2 incidence which are 5-50 times higher than reported by diagnostic tests and [6] obtain estimates of the SARS-CoV-2 incidence which are also 5 times higher than reported by diagnost…”
Result
- Authors
- Lu, F. S., Nguyen, A. T., Link, N. B., Davis, J. T., Chinazzi, M., Xiong, X., Vespignani, A., Lipsitch, M., & Santillana, M.
- Affiliations
- Stanford University (US)
- Boston Children's Hospital (US)
- Northeastern University (US)
- Harvard University (US)
- Corresponding author
- Santillana, Mauricio
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2020-04-23
- Volume
- 17
- Issue
- 6
- Pages
- e1008994-e1008994
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC8241061
- OpenAlex
- W3168278554
- Cited by
- 87
- References
- 80
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 6 · 2023: 11 · 2022: 16 · 2021: 27 · 2020: 14
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Maryland Shared Open Access Repository (USMAI Consortium) (submittedVersion, public-domain)
- Digital Repository at the University of Maryland (University of Maryland College Park) (public-domain)
- Funders
- Pfizer
- Sanofi
- National Institutes of Health (COVID-19)
- National Institute of General Medical Sciences (R01GM130668)
- U.S. National Library of Medicine (T32LM012411)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 pandemic, cumulative incidence, underreporting of cases, influenza-like illness surveillance, COVID-19 mortality, United States, Puerto Rico, Washington, D.C., public health response
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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