A novel modelling framework for immunity-driven epidemics of non-sterilising infections

Protecting populations against infections with non-sterilising immunity, such as COVID-19 and influenza, presents a major public health challenge. Population models used to guide public health and vaccination strategies are often incompatible with individual-level immunological and virological data. To address this gap, we develop a novel and flexible mathematical framework that links within-host immune and viral dynamics to between-host transmission, bridging multiple scales from individuals to populations. Our approach derives infectiousness from viral load and protection against reinfection from time-varying levels of immune factors, allowing population-level epidemiological trajectories to emerge as the summation of individual infectiousness and immunity. As an example, we use a phenomenological model of viral load quantified in a SARS-CoV-2 human challenge study, and a mechanistic model of binding antibody levels, fit to cross-sectional SARS-CoV-2-specific binding antibody levels post-vaccination. We demonstrate the ability of this framework to go beyond conventional compartmental models by predicting times to reinfection and the number of infections experienced by individuals. As a result, we show that immune responses fundamentally shape epidemic dynamics. Low correlations between antibody levels and protection lead to frequent reinfections and endemic circulation, whereas high correlations produce recurrent and explosive outbreaks. To demonstrate the potential of this modelling approach to estimate the protective power and reinfection dynamics across diverse immune histories, we recover the model parameters by fitting to simulated case data. This interdisciplinary approach provides new insights into the drivers of irregular epidemic patterns and can inform vaccination strategies for pathogens with non-sterilising immunity.