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.
It is particularly challenging to determine the true proportion of the population that has been previously exposed to SARS-CoV-2. Serological surveys that measure how many people have antibodies against the virus are a promising tool but results from such studies need to be interpreted carefully. Several studies following individuals over time after they’ve had a known infection were able to determine that antibodies are only measurable up to 6–9 months, on average. The immediate implication is that serological studies will inevitably under-estimate the number of people exposed, since some will have a lower antibody count when the study is conducted and test negative. We propose a method that takes this into account and informs the true level of exposure from triangulating serological data with mortality and test positivity data.
More details see our publication on PLOS Computational Biology here Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys
Authors: Siyu Chen, Jennifer A. Flegg, Lisa J. White, Ricardo Aguas