Modeling Seroreversion for Assessing SARS-CoV-2 Seroprevalence

Serology represents an important tool for determining prevalence and thus metrics such as fatality risk following infection. Seroreversion, or waning of antibody levels with time, can bias longitudinal assessments of prevalence. Though prior research examined seroreversion trends for particular immunoassays targeting antibodies against SARS-CoV-2, the viral cause of the COVID-19 pandemic, we know of no systematic determination of how much assay characteristics contribute to seroreversion risk. We present data quantifying the contribution of antigen target and assay design to seroreversion following an initial SARS-CoV-2 infection; this enables assay-specific seroreversion modeling for assays not included in our analysis but for which antigen target and assay design are known. We then use our modeled adjustments to account for seroreversion in serology-based longitudinal assessments of hospitalization and fatality risk following SARS-CoV-2 infection, shielding of the elderly from infection, and prevalence in children. We hope this framework can provide guidance in seroreversion modeling for future pathogen outbreaks. For more information visit: https://www.medrxiv.org/content/10.1101/2022.09.08.22279731v3 If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/