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Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys
Siyu Chen; Jennifer A Flegg; Lisa J White; Ricardo Aguas.
Affiliation
  • Siyu Chen; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom.
  • Jennifer A Flegg; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
  • Lisa J White; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield 5 Department of Medicine, University of Oxford, United Kingdom.
  • Ricardo Aguas; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Preprint in English | medRxiv | ID: ppmedrxiv-21249432
Journal article
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ABSTRACT
Accurate knowledge of accurate levels of prior population exposure has critical ramifications for preparedness plans of subsequent 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 decaying antibody titers over time, population exposure estimation methods 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 and simultaneously infer population exposure levels. The results indicate that the average time to seroreversion is six months, and that true exposure may be more than double the current seroprevalence levels reported for several regions of England.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study Language: English Year: 2021 Document type: Preprint
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