Heterogeneity across Scales: Modeling the Impact of Pathogen and Host Life Histories on the Dynamics of Acute Infection

Mathematical modeling is an essential tool in understanding and controlling the spread of infectious diseases. Although simple, ordinary differential equation models have provided many insights into dynamics at the population level, they are limited in their ability to capture key mechanisms underlying heterogeneous systems. In this thesis I consider aspects of pathogen and host life history that cause substantial heterogeneity, both within and between infected individuals, and can greatly improve our understanding of population dynamics when incorporated into existing mathematical frameworks. Chapter 1 opens with a general introduction, Chapters 2--5 present the research, and Chapter 6 concludes by synthesizing key themes and future directions.;First I consider host life history, in particular the impact of host dispersal on the spatial spread of disease. Mathematical models incorporating dispersal typically rely on highly resolved spatial data, which is challenging to obtain, particularly for wildlife populations. Chapter 2 addresses this problem through an outbreak of dolphin morbillivirus in the northwestern Atlantic. Despite limited information on population movements and disease incidence, I estimate key epidemiological parameters and demonstrate the importance of host migration in shaping the heterogeneous spatial distribution of disease. I contrast this work in Chapter 3 by using richer epidemiological data to explore dynamics of seasonal influenza. Within the relatively understudied region of Scandinavia, I show that seasonal outbreaks are more spatially synchronized than would be expected given the heterogeneous demographic distribution. Overall, these chapters address important challenges in identifying host factors that mediate spatial disease dynamics.;Next, I explore pathogen life history, and the impact of host-pathogen interactions on population dynamics. In Chapter 4 I show that incorporating heterogeneity in the strength of pathogen-conferred immunity can improve predictions of population-based disease models. Chapter 5 extends this to finer biological scales by explicitly modeling cellular interactions within an infected host. Using measles a case study, I extend previous models to incorporate dynamic feedbacks between the virus and host immune cells, and subsequently identify key drivers of acute viral clearance. Together, these chapters aim to contribute towards the increasing integration of within-host interactions in models of acute infections.