ABSTRACT

Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys   [open pdf - 1MB]

From the Document: "There are multiple sources of data giving information about the number of SARS-CoV-2 [severe acute respiratory syndrome coronavirus 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 [coronavirus disease] 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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Date:
2021-07-26
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Copyright:
Proceedings of the National Academy of Sciences of the United States of America. Posted here with permission. Document is under a Creative Commons license and requires proper attribution and noncommercial use to be shared: [https://creativecommons.org/licenses/by/4.0/]
Retrieved From:
Proceedings of the National Academy of Sciences of the United States of America: https://www.pnas.org/
Format:
pdf
Media Type:
application/pdf
Source:
PNAS (July 26, 2021), v.118 no.31, p.1-9; Proceedings of the National Academy of Sciences of the United States of America (July 26, 2021), v.118 no.31, p.1-9
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