Scaling up epizootic dynamics - Linking individual infection to spatial spread of a disease using Bayesian hierarchical approaches

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What determines how fast a disease spreads through a population may depend on how fast the disease spreads inside an infected individual so that processes within an individual are important for understanding disease transmission between individuals. Often, disease data are collected at a single scale of reference, such as within the individual, and other scales are ignored. This research will develop statistical methods and models that bring together information across scales ranging from the individual to the landscape. The experimental data used to develop these models will focus on an easily manipulated host-pathogen interaction between the fall armyworm and its lethal pathogen, a baculovirus. The models of transmission dynamics derived from the experimental data will quantify the extent that smaller scale processes affect larger scale dynamics and whether or not ignoring scale matters.In general, the modeling framework developed can be used to understand disease transmission, forecast transmission probability, and outbreak severity for other diseases including human pathogens. From an agricultural perspective, lethal baculoviruses represent a biological insecticide. This research will aid in their use and development. Additionally, this multidisciplinary project will involve a number of graduate students and postdoctoral researchers. The research conducted will train the next generation of statisticians, ecologists, and pathologists who will strengthen the ideas and methodologies generated from working across these disciplines.