Grant: Discovery Grants Program - Individual

Conventional wisdom is that infection spreads more readily in large communities, and that some diseases have seasonal patterns. While these patterns seem obvious, breakthroughs in understanding how these relationships arise from movement, propensity to aggregate, weather, and climate have been elusive. The proposed research describes how we will apply advances from the related disciplines of movement ecology, thermal ecology, and population dynamics to develop novel modelling frameworks and understanding. We will incorporate movement, seasonality and climate change into infectious disease models to resolve how community size and seasonal patterns arise, and to inform evidence-based decision-making for the control of Lyme disease, mosquito-borne illness, and rabies. The modelling frameworks that we develop will emphasize the essential features of disease spread. Simple, yet plausible, models are critical to inform evidence-based decision-making. Very complex model formulations are unreliable because when small uncertainties are many, these will "add up", and these uncertainties will compound due to the underlying potential for exponential growth that occurs for infectious disease models. Through participating in the proposed research, trainees will learn infectious disease modelling. Moreover, trainees will develop models that emphasize a strong grounding to real world knowledge and data. Trainees will learn to communicate how research results and recommendations might depend on assumptions and data gaps, while clearly describing the fundamental contributions of mechanistic modelling to infectious disease management and control.