{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:15:33Z","timestamp":1772172933246,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009374","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000}}],"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF DEB RAPID 2028632"],"award-info":[{"award-number":["NSF DEB RAPID 2028632"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"crossref","award":["P30 ES029067"],"award-info":[{"award-number":["P30 ES029067"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009374","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T13:58:04Z","timestamp":1631023084000},"page":"e1009374","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":35,"title":["Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7575-2368","authenticated-orcid":true,"given":"Weihsueh A.","family":"Chiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4158-7613","authenticated-orcid":true,"given":"Martial L.","family":"Ndeffo-Mbah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"pcbi.1009374.ref001","doi-asserted-by":"crossref","DOI":"10.17226\/25826","volume-title":"Evaluating Data Types: A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19","author":"National Academies of Sciences and Medicine E","year":"2020"},{"key":"pcbi.1009374.ref002","article-title":"Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020","author":"FP Havers","year":"2020","journal-title":"JAMA Intern Med"},{"issue":"29","key":"pcbi.1009374.ref003","doi-asserted-by":"crossref","first-page":"960","DOI":"10.15585\/mmwr.mm6929e1","article-title":"Population Point Prevalence of SARS-CoV-2 Infection Based on a Statewide Random Sample\u2014Indiana, April 25\u201329, 2020","volume":"69","author":"N Menachemi","year":"2020","journal-title":"MMWR Morb Mortal Wkly Rep"},{"key":"pcbi.1009374.ref004","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.annepidem.2020.06.004","article-title":"Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York","volume":"48","author":"ES Rosenberg","year":"2020","journal-title":"Ann Epidemiol"},{"issue":"10259","key":"pcbi.1009374.ref005","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/S0140-6736(20)32009-2","article-title":"Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study","volume":"396","author":"S Anand","year":"2020","journal-title":"Lancet"},{"key":"pcbi.1009374.ref006","article-title":"Estimated SARS-CoV-2 Seroprevalence in the US as of September 2020","author":"KL Bajema","year":"2020","journal-title":"JAMA Intern Med"},{"key":"pcbi.1009374.ref007","unstructured":"CDC COVID Data Tracker [Internet]. 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