{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:10:49Z","timestamp":1772806249749,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009436","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000}}],"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP200100747"],"award-info":[{"award-number":["DP200100747"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007421","name":"Li Ka Shing Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007421","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Oxford\u2019s COVID-19 Research Response Fund","award":["BRD00230"],"award-info":[{"award-number":["BRD00230"]}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1193472"],"award-info":[{"award-number":["OPP1193472"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Accurate knowledge of prior population exposure has critical ramifications for preparedness plans for future SARS-CoV-2 epidemic waves and vaccine prioritization strategies. Serological studies can be used to estimate levels of past exposure and thus position populations in their epidemic timeline. To circumvent biases introduced by the decay in antibody titers over time, methods for estimating population exposure should account for seroreversion, to reflect that changes in seroprevalence measures over time are the net effect of increases due to recent transmission and decreases due to antibody waning. Here, we present a new method that combines multiple datasets (serology, mortality, and virus positivity ratios) to estimate seroreversion time and infection fatality ratios (IFR) and simultaneously infer population exposure levels. The results indicate that the average time to seroreversion is around six months, IFR is 0.54% to 1.3%, and true exposure may be more than double the current seroprevalence levels reported for several regions of England.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009436","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T14:37:30Z","timestamp":1632148650000},"page":"e1009436","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":25,"title":["Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-2527","authenticated-orcid":true,"given":"Siyu","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8809-726X","authenticated-orcid":true,"given":"Jennifer A.","family":"Flegg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6523-185X","authenticated-orcid":true,"given":"Lisa J.","family":"White","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6507-6597","authenticated-orcid":true,"given":"Ricardo","family":"Aguas","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"pcbi.1009436.ref001","article-title":"The socio-economic implications of the coronavirus and COVID-19 pandemic: a review","author":"M. 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