When hosts gather: how extreme seasonal aggregation affects epidemiological outcomes

Wildlife aggregate for many reasons (e.g. reproduction, feeding) and at times these aggregations can be extreme, with host densities increasing several orders of magnitude. While the impact of seasonality on infectious disease dynamics is well studied, few-if any-studies have explicitly examined how extreme aggregation affects key epidemiological outcomes. Here we consider an epidemic in a closed SIR (Susceptible-Infectious-Recovered) metapopulation with a hub-satellite structure, where seasonal movement into the hub follows a modified Gaussian function. We numerically explore how aggregation duration and timing shape two outcomes: final size and peak prevalence. We find a narrow set of circumstances and pathogens for which even extreme aggregation materially alters these outcomes. When aggregation coincides with, or begins just prior to, infection introduction, aggregation can strongly affect pathogens with $R_0\approx 1$ or $R_0<1$ , enabling epidemics that would otherwise fade. Effects are strongest under density-dependent transmission, where contact rate scales with local density; frequency-dependent transmission renders aggregation negligible. High transmissibility $\left(R_0\gg 2\right)$ minimises aggregation's impact because most susceptibles are infected regardless of density changes.