Speaker
Description
Standard epidemic models tend to assume that human behaviour is fixed, rational, or slow to change. Reality is messier. During COVID-19 and beyond, behaviour has proven fast-moving, socially contagious, and often emotionally driven. People respond to risk, to each other, and to policy - sometimes amplifying interventions, sometimes undermining them. We present a modelling framework that treats behaviour as part of the epidemic system itself, not an external input. Transmission dynamics are coupled to time-varying behavioural indices (e.g. trust, adherence), which evolve through feedback from incidence, peer influence, and bounded policy responses. This creates a two-way interaction: epidemics shape behaviour, and behaviour reshapes epidemics. Using structured perturbations of behavioural drivers under realistic “policy budgets,” we generate ensembles of counterfactual scenarios and quantify uncertainty in outcomes such as peak incidence and healthcare demand. Even small shifts in behavioural response can produce large and nonlinear differences in epidemic trajectories, particularly around critical periods such as pre-peak intervention timing. The key message is simple: identical policies can lead to very different outcomes, depending on how people respond. By embedding behaviour directly into mechanistic models, we move towards a more realistic and policy-relevant understanding of epidemic dynamics - one where human response is not noise, but signal.
