Evolutionary emergence of infectious diseases in heterogeneous host populations

Abstract

Emergence and re-emergence of pathogens are notoriously difficult to predict. The erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic. For instance, mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence. Yet, the ecological factors that govern such evolutionary emergence remain elusive both because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions. Here we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence, with or without pathogen evolution. We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts. We also show that lower pathogen inoculum size and higher diversity of host resistance decrease the probability of evolutionary emergence. Crucially, we present experimental confirmations of these predictions using lytic bacteriophages infecting their bacterial hosts containing diverse CRISPR-Cas immune defenses. We discuss the implications of these results for cross-species spillover and for the management of emerging infectious diseases.\n\nSignificance statementCan we predict the emergence of infectious diseases? The probability that an epidemic breaks out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence. Forecasting pathogen emergence thus requires a good understanding of the interplay between epidemiology and evolution taking place at the onset of an outbreak. Here, we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence. We use this model to predict the impact of the fraction of susceptible hosts, the inoculum size of the pathogen and the diversity of host resistance on pathogen emergence. Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions.