Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom.
School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
PLoS Comput Biol. 2021 Sep 20;17(9):e1009436. doi: 10.1371/journal.pcbi.1009436. eCollection 2021 Sep.
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.
准确了解既往人群暴露情况，对于制定未来 SARS-CoV-2 流行波的防范计划和疫苗优先接种策略具有重要意义。血清学研究可用于估计既往暴露水平，从而在流行时间线上定位人群。为了避免因抗体滴度随时间衰减而导致的偏差，用于估计人群暴露的方法应考虑血清学转换，以反映随着时间的推移，血清流行率的变化是由于近期传播导致的增加和由于抗体衰减导致的减少的净效应。在这里，我们提出了一种新的方法，该方法结合了多个数据集（血清学、死亡率和病毒阳性率比）来估计血清学转换时间和感染病死率（IFR），并同时推断人群暴露水平。结果表明，血清学转换的平均时间约为六个月，IFR 为 0.54%至 1.3%，而真实的暴露水平可能是英格兰几个地区目前报告的血清阳性率的两倍以上。
