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DISCOVERING SPATIO-TEMPORAL MODELS OF THE SPREAD OF WEST NILE VIRUS
Citation:
ORME ZAVALETA, J., J. JORGENSEN, B. D'AMBROSIO, E. ALTENDORF, AND P. ROSSIGNOL. DISCOVERING SPATIO-TEMPORAL MODELS OF THE SPREAD OF WEST NILE VIRUS. RISK ANALYSIS. Blackwell Publishing, Malden, MA, 26(2):413-422, (2006).
Impact/Purpose:
to determine plausible mechanisms and patterns (temporal and geospatial) of disease spread for West Nile Virus
Description:
Understanding interactions among pathogens, hosts, and the environment is important in developing rapid response to disease outbreak. To facilitate the development of control strategies during an outbreak, we have developed a tool for utilizing data to its maximum extent to determine plausible mechanisms and patterns (temporal and geospatial) of disease spread. These data are often observational and collected during independent surveys. We used a machine-learning, model discovery technique, Relational Bayesian Networks (RBN) to construct quantitative and biologically consistent models of West Nile Virus (WNV) spread. Survey data on WNV cases in mosquitoes, horses, humans, and birds in Maryland collected during 2001 and 2002, along with tire storage facilities (as an indicator of mosquito breeding habitat) were explored using this technique. Our results indicate that infected birds are indicators for positive mosquito pools and human cases. This tool shows promise in determining complex community interactions relevant to disease transmission that could guide monitoring and control strategies during the early stages of an outbreak or during an ongoing outbreak of a rare disease.