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Abstract:
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Disease surveillance data help epidemiologists, animal and plant pathologists, and ecologists identify and evaluate the spread of infectious disease in various populations. While methods exist to identify likely sources of a pathogen in simple circumstances or forecast the spread of an infectious disease in broad terms, few methods explicitly estimate the spatio-temporal origins of a pathogen that caused an epidemic (i.e., the number, locations, and times that a pathogen was introduced). We outline a likelihood-based method and a staged approximate Bayesian model averaging (SABMA) method to obtain this inference using multiple diffusion processes to represent the spread of a pathogen through a population. We evaluate the predictive performance and inference from the SABMA method compared to the likelihood-based method via a simulation experiment. We then apply both methods to two sets of disease surveillance data on chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus); the first in the lower peninsula of Michigan in the U.S., and the second in southern Wisconsin and northern Illinois in the U.S.
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