Reimagining the serocatalytic model for infectious diseases: a case study of common coronaviruses

Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and serore-version (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) throughout the lifespan. These methods identified four (NL63, 229E, OC43) to five (HKU1) distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. For each sHCoV, we fit adapted, multi-compartment serocatalytic models across 10 scenarios with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for a gradient of seropositivity as well as host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses across sHCoVs, finding that the time for a seronegative individual to become seropositive ranges from 2.33-4.07 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We also find that despite frequent infection and strong serological responses, it is rare for an individual to remain seropositive throughout the lifetime. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.