The field of mathematical epidemiology aims to understand the distribution of diseases in populations using mathematical models. Building models involves distilling the biological facts of a disease system and using mathematical theory to describe the system dynamics. Similarly, molecular epidemiology relies on population genetic theory to link pathogen genetic data to epidemiological inference. This thesis aims to both understand the biological principles that drive the evolution of mycobacterial pathogens, but to also determine which biological processes can be described by current theory, and which questions require new analytic approaches to fully describe. Here we describe a new hidden Markov model to characterize the progression of Mycobacterium avium subsp. paratuberculosis infection, the cause of Johne’s disease in ruminants through disease states. We find a minority of infected animals remain low-shedders throughout the duration of infection, whereas others progress to a high shedding disease state. Our modeling framework could be used to predict future progression patterns based on curing shedding state, which could aid in decision making on the farm. Next we study the pangenome of Mycobacterium bovis, the cause of bovine tuberculosis. Recent studies suggest that M. bovis has a large accessory genome, yet there is no known mechanism for horizonal gene transfer in M. bovis. We found significant errors in accessory gene classification, correcting for errors, we show that M. bovis has a much smaller accessory genome than previously described with little gene content variation generated over outbreaks. Finally, we study the within-host evolution of M. bovis and employ flexible forward genetic simulation tools to evolutionary processes that deviate from standard evolutionary models. We found that the distribution of mutations that evolve de novo within animal hosts is random, suggesting M. bovis evolution is driven by drift at short time scales. We also find that the combined effect relatively rapid mutation rates and diversity reducing infection bottlenecks and skewed offspring distributions best describe within host evolution M. bovis. Lastly, we explore the effects of a rapid mutation rate on the evolution of antimicrobial resistance genotypes in the absence of antimicrobial use.