Structural causal influence captures the forces of social inequality in models of infectious disease

Mathematical models are central to understanding disease transmission for host-pathogen systems across the biosphere. However, most existing frameworks do not explicitly treat host population heterogeneity as a formal driver of variation in transmission dynamics. This gap is most visible in human disease, where social inequalities are well known to structure infectious disease risk, but applies broadly: ecological, behavioural and demographic structure shapes transmission in non-human host populations as well. Here, we introduce a new metric, structural causal influence, which uses causal analysis to quantify how subpopulations contribute to overall transmission through structural differences in exposure, susceptibility or recovery. Using a multi-population model, we show that even a population whose isolated reproduction number lies below one can be drawn into a sustained epidemic through minimal contact with a more affected group.