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When hosts gather: how extreme seasonal aggregation affects epidemiological outcomes
Daniel N R Longmuir1,2, Simon Johnstone-Robertson1, Andrew J Hoskins3,4
1Mathematical Sciences, STEM College, RMIT University, Melbourne, Australia.
Infectious Disease Modelling
|July 1, 2026
Summary
Extreme wildlife aggregation can trigger epidemics, especially for pathogens with low transmissibility (R0≈1 or R0<1). This effect is most pronounced under density-dependent transmission when aggregation aligns with infection introduction.
Area of Science:
- Ecology
- Epidemiology
- Mathematical Biology
Background:
- Wildlife aggregations, driven by factors like reproduction and feeding, can drastically increase host densities.
- While seasonality's impact on infectious diseases is known, the effect of extreme aggregation on epidemiological outcomes remains understudied.
Purpose of the Study:
- To investigate how extreme wildlife aggregation influences key epidemiological parameters, specifically final epidemic size and peak prevalence.
- To explore the role of aggregation timing and duration in shaping disease dynamics within a metapopulation model.
Main Methods:
- A closed Susceptible-Infectious-Recovered (SIR) metapopulation model with a hub-satellite structure was employed.
- Seasonal movement into the hub was modeled using a modified Gaussian function.
- Numerical simulations were used to explore the impact of aggregation on epidemic outcomes.
Main Results:
- Extreme aggregation significantly alters epidemic outcomes only under specific conditions, particularly when aggregation coincides with or precedes infection introduction.
- Pathogens with a basic reproduction number (R0) close to 1 or less than 1 are most affected, with aggregation enabling epidemics that might otherwise decline.
- The impact of aggregation is strongest under density-dependent transmission; frequency-dependent transmission renders aggregation effects negligible.
- High transmissibility (R0 >> 2) minimizes aggregation's impact as most susceptible individuals are infected regardless of density changes.
Conclusions:
- Extreme wildlife aggregation is a critical factor in disease dynamics, but its influence is context-dependent, particularly concerning pathogen transmissibility and transmission mode.
- Understanding aggregation patterns and timing is crucial for predicting and managing wildlife epidemics, especially for pathogens with moderate to low transmissibility.
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