ArticleProceedings of the National Academy of Sciences of the United States of America2021
Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Agreement between seroprevalence- and model-based estimates of COVID-19 burden.Global health action · 2026Article
- Low SARS-CoV-2 Positivity in a Large, Diverse US-Based Study of Persons Affected by HIV.AIDS and behavior · 2026Article
- Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections.Statistics in medicine · 2026Article
- How Many Veteran COVID-19 Cases Were There during the Pandemic?Journal of medical virology · 2026Article
- Multilevel regression and poststratification interface: an application to track community-level COVID-19 viral transmission.Population health metrics · 2026Article
- Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling.Nature communications · 2026Article
- Retrospective estimate of COVID-19 infections in nine Colombian cities in 2020.Epidemiology and infection · 2026Article
- The tale of two assumptions: incorporating healthcare-seeking behaviour in epidemic forecasting.BMC infectious diseases · 2025Article
- Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States.BMC global and public health · 2025Article
- Estimated transmission dynamics of SARS-CoV-2 variants from wastewater are unbiased and robust to differential shedding.Nature communications · 2025Article
- Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter.Epidemics · 2025Article
- Asymmetric limits on timely interventions from noisy epidemic data.Communications physics · 2025Article
- Timing of Infection as a Key Driver of Racial/Ethnic Disparities in Coronavirus Disease 2019 Mortality Rates During the Prevaccine Period.Open forum infectious diseases · 2025Article
- Severe outcomes of COVID-19 among adults with increased risk conditions: A population-based observational study.PloS one · 2025Observational
- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.American journal of epidemiology · 2024Article
- Missing data and missed infections: investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts.American journal of epidemiology · 2024Article
- Estimated relative potential for airborne SARS-CoV-2 transmission in a day care centre.Heliyon · 2024Article
- Inferring COVID-19 testing and vaccination behavior from New Jersey testing data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Estimating actual SARS-CoV-2 infections from secondary data.Scientific reports · 2024Article
1 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible-Infected-Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.
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