An Eco-Evolutionary Framework to Predict Long-term Strain Diversity and Turnover in Infectious Diseases

Pathogen strain diversity varies widely across diseases, yet the processes that govern its emergence, maintenance, and turnover remain unclear. We present MultiSEED, a fast numerical framework that captures strain dynamics across ecological and evolutionary timescales. Unlike previous models, MultiSEED distinguishes between transient and established strains, allowing classification of diseases into regimes of extinction, replacement, or coexistence. When applied to influenza A, HIV-1, pneumococcus, and malaria, the model reproduces observed strain diversity and turnover while revealing the key drivers of these differences. We show that susceptible host replenishment is central to long-term strain persistence, host population size and R ₀ shape overall diversity, and innovation rates strongly influence dynamic regimes, with their combined interactions ultimately determining pathogen strain diversity and turnover. This unifying framework can be readily applied across systems, enabling the rapid prediction of pathogen responses to environmental and demographic changes and guiding strategies for disease control.