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. Unlike other infections, COVID-19 has produced irregular epidemic dynamics that are not fully explained by seasonal behavioural patterns. New variants and waning immunity require repeated vaccination, but the population models used to guide these strategies are often in-compatible 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 combines a mechanistic description of viral load and antibody responses, fit to qPCR data from a COVID-19 human challenge study, with population-level transmission dynamics. Specifically, we link infectiousness to viral load and protection against reinfection to time-varying level of immune factors, allowing population-level epidemiological trajectories to emerge as the summation of individual infectiousness and immunity. Using previously parameterised models of COVID-19 antibody dynamics and a mechanistic model of viral load, we compare this framework to conventional compartmental models and demonstrate its ability to predict times to reinfection and the number of infections experienced by individuals.

We show that the immune profile within individuals fundamentally shapes epidemic dynamics. Successive epidemic waves consist of overlapping immune histories, with their characteristics determined by the degree of cross-protection between infections and vaccination. Low correlations between antibody levels and protection lead to frequent reinfections and endemic circulation, whereas high correlations produce recurrent, explosive outbreaks. Model parameters can be recovered by fitting to simulated case data, indicating that the framework can be adapted to real-world surveillance data to estimate the protective power and dynamics of diverse immune histories. This transdisciplinary approach provides new insights into the drivers of irregular epidemic patterns and can inform vaccination strategies for pathogens with non-sterilising immunity.