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

Strain diversity and regimes of strain dynamics vary significantly among diseases. Understanding the eco-evolutionary forces that shape pathogen strain diversity is central to predicting their response to environmental and demographic changes and informing disease control strategies. However, most existing models focus on short-term invasion processes, which are insufficient for understanding long-term dynamics. Here, we develop Multi-Strain Eco-Evo Dynamics (MultiSEED), a unified theoretical framework that integrates a deterministic n -strain SIRS (Susceptible-Infected-Recovered-Susceptible) model with continuous-time stochastic processes to approximate multi-strain disease dynamics across both ecological and evolutionary scales. MultiSEED accounts for key mechanisms often overlooked in previous models, such as finite strain-specific immunity, mixed infections and within-host competition. Additionally, the model can distinguish between transient and persistent strains and classify strain dynamics into regimes of extinction, replacement, or long-term coexistence. We apply MultiSEED to four human pathogens (influenza A, HIV-1, pneumococcus, and malaria) that differ widely in life history and transmission ecology. The model accurately reproduces observed levels of strain diversity and lifespan. The model also reveals how interactions among host population size, transmissibility, cross-immunity, and immune loss collectively govern strain diversity, whereas strain innovation rates have a more significant impact on the regimes of strain dynamics. The model further highlights how parameter interactions near the persistence threshold are highly complex, requiring MultiSEED evaluations for regime predictions. By bridging short-term epidemiological processes with long-term evolutionary dynamics, MultiSEED offers a quantitative framework for estimating the emergence, maintenance, and turnover of pathogen strain diversity across a range of infectious diseases and ecological contexts.