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Edited by Adrian E. Raftery, University of Washington, Seattle, WA, and approved December 2, 2020 (received for review July 2, 2020)
Significance
Infection with the novel coronovirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide pandemic of COVID-19 disease. Efforts to design local, regional, and national responses to the virus are constrained by a lack of information on the extent of the epidemic as well as inaccuracies in newly developed diagnostic tests. In this study we analyze data from testing randomly selected Indiana state residents for infection or previous exposure to SARS-CoV-2 and derive estimates of the statewide COVID-19 prevalence in an attempt to address potential biases arising from nonresponse and diagnostic testing errors.
Abstract
From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.
Footnotes
- ↵1To whom correspondence may be addressed. Email: cyiannou{at}iu.edu.
Author contributions: C.T.Y., P.K.H., and N.M. designed research; C.T.Y. performed research; C.T.Y. analyzed data; C.T.Y. wrote the paper; P.K.H. and N.M. edited the paper; N.M. was principal investigator of the Indiana State contract to perform the study; and P.K.H. and N.M. liaised with the Indiana Department of Health and the Governor’s Office.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
See online for related content such as Commentaries.
Data Availability.
Anonymized data have been deposited in GitHub (https://github.com/cyiannou/IDOH-STUDY/).
- Copyright © 2021 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing
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