Host behaviour driven by awareness of infection risk amplifies the chance of superspreading events

Abstract

We demonstrate that heterogeneity within host populations in the perceived risks associated with infection can amplify the chance that superspreading occurs during the crucial, early stages of an epidemic. Under our behavioural model, individuals who are less concerned about the risks of infection are more likely to be infected and to attend larger sized events. For directly communicable diseases, such as COVID-19, this leads to infections being introduced at rates above the population prevalence to events that are most conducive to superspreading, for fixed overall transmission levels. We develop a computational framework for evaluating within-event risks and derive a small-scale reproduction number to measure how introductions and transmission heterogeneities determine the number of infections resulting from an event of given duration. This generalises earlier frameworks and both quantifies and clarifies how event-scale dynamics depend on population-level characteristics. As event duration and size increase, our reproduction number converges to the widely used basic reproduction number, R0. We show that even moderate levels of heterogeneity in the perceived risks associated with infection can substantially increase the risk that disproportionately large numbers of infections are generated at larger events, as compared to assuming homogeneous perceptions of risk across the host population. Behavioural dynamics, which remain understudied in the context of epidemiological modelling, are essential to consider when assessing the risk posed by an invading pathogen in the early stages of an infectious disease epidemic.