The West Nile Virus (WNV) is an infectious disease spreading rapidly throughout the United States, causing illness among thousands of birds, animals, and humans. The broad categories of risk factors underlying WNV incidences are: environmental, socioeconomic, built-environment, and existing mosquito abatement policies. Computational neural network (CNN) model was developed to understand the occurrence of WNV infected dead birds because of their ability to capture complex relationships with higher accuracy than linear models. In this paper, we describe a method to interpret a CNN model by considering the final optimized weights. The research was conducted in the Metropolitan area of Minnesota, which had experienced significant outbreaks from 2002 till present. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Ghosh, D., & Guha, R. (2010). A risk factor analysis of west nile virus: Extraction of relationships from a neural-network model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6007 LNCS, pp. 208–217). https://doi.org/10.1007/978-3-642-12079-4_27
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