Distingued Research Professor
University of Georgia
Forecasting the spread of epidemics or invasive organisms at continental scales is complicated by processes acting at multiple scales on varied landscapes. Yet such forecasts are crucial for managing and halting epidemic spread. This project uses computational tools from mathematics, computer science, epidemiology and ecology to understand and predict the spread of a devastating disease of cave-dwelling bats, white-nose syndrome, which is caused by the soil fungus, Pseudogymnoascus destructans. This project addresses two specific problems. First, it seeks to identify the corridors along which white-nose syndrome is likely to spread and the locations most at risk, using the spatial pattern of past spread and an approach called network analysis. New methods will be used that will speed up computations, turning the computer model into a more useful tool in management and control. Second, the local, random processes that affect individual caves will be linked to the process of spread at regional to continental scales. This multi-scale linkage will be the first to use a simulation modeling method, called the equation-free approach, to estimating epidemics when data are limited, which is most of the time in such circumstances. The combining of these two approaches will provide a more accurate understanding how white-nose syndrome has spread already and how it is likely to spread in the future. The economic benefit that bats provide to agriculture in the United States by eating insects has been estimated to be $4-50 billion each year. This project will contribute tools for controlling the spread of white-nose and the resulting losses to agriculture, as well as the serious impacts it has on the bats themselves. It is expected that the approaches and tools developed in this project for white-nose syndrome could also be applied to other wildlife diseases, as well as for the regional and continental spread of human pathogens. The scientists involved will communicate their practical findings directly to the land and natural resources managers and to the public. The project will also have educational benefits through the transfer of skills from the field of mathematics to ecology and epidemiology. The project will also provide exciting research opportunities for undergraduate and graduate students.