DISSERTATION RESEARCH: Scaling within host interactions to epidemic patterns

William Marsh Rice University, Houston TX

Investigators

Abstract

Parasites regulate the dynamics of almost all populations, and the vast majority of hosts (including humans) are infected by multiple parasite species at the same time. Do infections by multiple parasite species increase the risk of epidemics in host populations? This research will establish a general framework that predicts how co-infections affect epidemics in host populations. Results will help predict the consequences of concurrent emerging parasites and will inform strategies to help curb epidemics that threaten wildlife, animal stocks, agricultural crops, and human populations. The project will extend the research and training of a doctoral student by supporting two new experiments that will improve existing mathematical models. The researchers will engage a diverse range of students in the Houston Public Schools through lectures on infectious disease and participation in the design of experiments and analysis of resulting data. When multiple parasites infect hosts, the order of infection likely plays an important role because it can determine transmission rates and host mortality. The effects of arrival order - or priority effects - are well documented within single hosts but rarely incorporated in classical models to predict and understand multi-parasite epidemics. The goal of this project is to determine (1) how the relative timing of infections alters epidemics in multi-parasite systems and whether multiple infections increase variation in outcome of epidemics, and (2) test whether this variation can be predicted with mechanistic models that incorporate the consequences of co-infection from single-host data. These goals will be accomplished using a combination of experiments and models. The first experiment will examine multi-parasite epidemics with multiple arrival orders of parasites and doubly infected and singly infected populations. This will determine how priority effects alter epidemic patterns at the host population level. A series of single host infection experiments will be conducted to parameterize a predictive epidemic model that accounts for the infection history of hosts and how this history influences interactions among co-infecting parasites and hosts. Comparing model predictions to empirical epidemics will test whether variation in epidemics patterns in natural populations can be predicted from individual host data when accounting for the infection history of hosts. The predictive power of this model will be compared to traditional models that do not include priority effects.