Modeling the dynamics of SARS-CoV-2 immunity waning, antigenic drifting, and population serology patterns

Reinfection with SARS-CoV-2 can result from either waning immunity, a drift in the virus that escapes previously stimulated immunity, or both. The nature of such reinfection risks will affect the choice of control tactics and vaccines. We constructed an SIR transmission model of waning and drifting that can be fitted to cross-neutralization serological data. In this model, waning occurs in individuals who have recovered from previous infections while drifting occurs during transmission to a previously infected individual. Interactions at the population level generate complex dynamics that cause drifting to occur in unanticipated but explainable ways across waning and drifting parameter sets. In particular, raising the fraction of transmissions where drifting occurs slows the rise of drifted strains to high levels and changes the equilibrium distribution of strains from {cup} shaped (extreme strains dominate) to {cap} shaped (central strains dominate). In {cup} shaped parameter regimes, endemic infection levels can rise after many years to above the original epidemic peak. The model simulates results from cross-neutralization assays given sera from previously infected individuals when multiple drifted strains are used in the assays. Fitting the model to such assay data can estimate waning and drifting parameters. Given the parameters, the model predicts infection patterns. We propose a process for using fits of our model to serological and other data called Decision Robustness and Identifiability Analysis (DRIA). This can inform decisions about vaccine options such as whether to prepare for changes in vaccine composition because the virus is changing to escape immunity. HighlightsO_LIWe construct and analyze a transmission system model that integrates immunity waning and antigenic drifting. We found no other model that integrates both. C_LIO_LIThe model reveals strong joint effects of waning and drifting. For example, waning is essential for drifting. But once drifting starts, it generates its own momentum. C_LIO_LIThe model generates unexpected but explainable phenomena such as: A) Given low drifting parameters, strains become increasingly different from each other, but given high drifting parameters, they merge together. B) Higher drifting rates accelerate the appearance of new strains but slow the expansion of those new strains. C_LIO_LIThe model generates assay data for neutralization across drifted strains. This enables population serological analyses to estimate waning and drifting parameters. C_LIO_LIPredicting drifting through population serology analyzed by fitting models to the data has potential to better inform vaccine strain choices than the approach taken for influenza which focuses on analysis of large numbers of virus strains over time. C_LIO_LIModel elaboration using a strategy of Decision Robustness and Identifiability Analysis will inform decisions about vaccine design and use and provides insights on resistance to infection by age. C_LI