How do ecological processes shape the transmission and severity of disease in humans

"Ecological processes can shape disease dynamics in humans and human populations. For example, at the ecosystem level, the distribution and abundance of insect vectors and alternative hosts can determine the frequency and severity of arbovirus outbreaks among humans. At the individual level, the ecological dynamics of host-associated (eg, gut or respiratory) microbiomes influence whether hosts suffer from outcomes including irritable bowel disease, asthma, arthritis, obesity, and autism1. Mathematical and statistical models are needed to understand mechanisms, predict outcomes, and plan interventions. For example, ecosystem models can predict how climate or land use change are likely to impact pathogen and vector densities, pathogen interactions with hosts, and disease prevalence. Community dynamics models predict the trajectories of microbiomes and thus the progression of disease in individuals. Understanding the likely dynamics of diseases or outbreaks can allow practitioners to prevent or prepare for worst-case scenarios.

In this PhD, the student will student will master a range of quantitative approaches and gain experience with a variety of biological systems as they develop models to answer three distinct questions:

1. How will changes in the density and species composition of vector communities driven by land use change affect the prevalence of arbovirus diseases in humans? The student will develop Ross-MacDonald models that allow for multiple species of hosts and vectors (eg, 2) and for pathogen adaptation to host and vector species. (Advisors: Gilman, Walton)

2. How do bacteriophages shape the diversity and stability of microbiomes, and how does this affect asthma progression? The student will build on Lotka-Volterra models developed by Gilman and Papadopoulos to understand how phages impact asthma via the microbiome under the Anna Karenina Principle3,4. (Advisors: Gilman, Papadopoulos)

3. How will climate change affect the distribution of fungal disease in humans? The student will develop mechanistic and phenomenological (eg, climate envelope) models5 to predict future distribution of fungal disease working from historical data on infection frequency, accounting for the uneven collection of historical data over space and time. (Advisors: Gilman, Bromley)"