When the process of transmission occurs on a similar timescale as the process of pathogen evolution, genetic sequences of the pathogen may contain a signature of disease dynamics. Most methods that aim to infer dynamics from pathogen genetic sequences operate in two disjoint steps, first estimating the phylogeny of the pathogen and then fitting models of disease dynamics to properties of the estimated phylogeny. Logical inconsistency in demographic assumptions underlying the two stages of inference may create bias in resulting parameter estimates. Joint inference of disease dynamics and phylogeny ensures consistent assumptions, but few methods for joint inference are currently available.
In this thesis, we first demonstrate through simulation that errors in phylogenetic reconstruction may cause bias in parameter estimates derived from two-stage inference. This result underscores the need for methodology for joint inference of the transmission model and the pathogen phylogeny. We then propose a new method for joint inference. This method is comprised of a class of algorithms that use sequential Monte Carlo to maximize and estimate likelihoods. As this method is simulation-based, it allows for fitting a wide range of models. We explore the feasibility of this approach through simulation and a study on stage-specific infectiousness of HIV in Detroit, MI. We also demonstrate the flexibility of this approach with a study on simulated outbreak of Vancomycin-resistant Enterococcus in a hospital setting.