UCLA

The evolution and emergence of pathogens is subject to a wide array of ecological and evolutionary forces acting at multiple mechanistic scales. Theoretical work regarding pathogen emergence has largely neglected the influence of selection acting at multiple scales, and methodologies for analyzing cross-scale data are scarce. Chapter one presents a novel cross-scale model of evolutionary emergence, considering selection the effect of selection at within-host and between-host scales on the evolutionary emergence of novel pathogens. The stochastic population genetic model demonstrates the complexities that arise when considering pathogen emergence at multiple scales: positive correlations between fitness can unexpectedly hasten emergence, conflicts across scales can lead to evolutionary dead ends, and evolution of the pathogen can be disproportionately influenced by neighboring genotypes in the fitness landscape. Chapter two builds upon the foundation of the stochastic modeling framework introduced in chapter one, and explores the application to drug resistance. This analysis shows that varying selection regimes, arising from prophylactic drug use and intermittent treatment compliance, interact with the fitness of the resistant genotype to create trade-offs between epidemic control and drug resistance outcomes. Chapter three addresses the empirical domain of cross-scale analysis, and presents a framework for jointly estimating within-host and between-host fitness using a Bayesian data augmentation approach. Data at the within-host and between-host scales from influenza A transmission experiments in ferrets are used as a real-world case study to explore how fitness values at the two scales are correlated, and to determine how these parameter estimates can aid in predicting influenza transmissibility in humans. Despite small sample sizes, this approach was validated using simulated data, demonstrating a promising methodology for analyzing pathogen data at multiple scales. The body of work presented here introduces novel frameworks for theoretical development, presents new methodologies for analyzing pathogen data, and highlights the importance of considering multiple scales of selection acting on pathogen evolution and emergence.