Speaker
Description
Assessing epidemic risk, both the probability that pathogen introductions lead to major outbreaks and the trajectory of ongoing circulation, often relies on population-level indicators derived from compartmental models. These frameworks typically assume homogeneous mixing within epidemiological compartments, yet real populations are structured by space, behavior, and heterogeneous exposure. Such heterogeneities complicate both the description of epidemic dynamics and the implementation of public health interventions. How should these different sources of heterogeneity be handled for mathematical models to inform public health policymaking? In this talk I discuss when heterogeneity must be explicitly represented, when it can be simplified, and when it may mislead intervention design. Different forms of heterogeneity play different roles. Variation in individual exposure risk can shape transmission patterns but does not necessarily justify targeted interventions: broad non-selective distribution of preventive measures may outperform strategies aimed at high-risk groups, as shown for HIV prevention. Even when heterogeneity is present, it may sometimes be handled through aggregation, for instance by combining spatial data with surveillance information to improve epidemic monitoring for respiratory pathogens. Together these examples highlight which forms of heterogeneity matter for estimating epidemic risk and how they can be incorporated into models used for policy.
