An immunity-driven modelling framework for epidemics of non-sterilizing infections

Protecting populations against pathogens that induce non-sterilizing immunity remains a major public health challenge. However, conventional mathematical models are often incompatible with within-host data, offering limited insight into how immune responses drive epidemics. To address this gap, we develop a modular, data-driven mathematical framework that links immunological and virological dynamics to population-level transmission. Our approach derives infectiousness from viral load and protection against reinfection from time-varying immune responses, allowing epidemic trajectories to emerge from the summation of individual-level processes. As an example, we use viral load quantified in a SARS-CoV-2 human challenge study and binding antibody levels post-vaccination against SARS-CoV-2. The framework captures individual-level infection dynamics and shows that immune responses fundamentally shape epidemic trajectories. We show that weak correlations between antibody levels and protection lead to frequent reinfections and endemic circulation, whereas strong correlations generate recurrent explosive outbreaks. We fit the model to simulated case data to demonstrate its ability to recover underlying protection and reinfection dynamics. Applied to real-world data, this framework could provide new insights into the drivers of epidemic patterns and inform vaccination strategies for pathogens with non-sterilizing immunity.