Use of genetic algorithm and neural network approaches for risk factor selection: A case study of West Nile virus dynamics in an urban environment

Abstract The West Nile virus (WNV) is an infectious disease spreading rapidly throughout the United States, causing illness among thousands of birds, animals, and humans. Yet, we only have a rudimentary understanding of how the mosquito-borne virus operates in complex avian–human environmental systems coupled with risk factors. The large array of multidimensional risk factors underlying WNV incidences is environmental, built-environment, socioeconomic, and existing mosquito abatement policies. Therefore it is essential to identify an optimal number of risk factors whose management would result in effective disease prevention and containment. Previous models built to select important risk factors assumed a priori that there is a linear relationship between these risk factors and disease incidences. However, it is difficult for linear models to incorporate the complexity of the WNV transmission network and hence identify an optimal number of risk factors objectively. There are two objectives of this paper, first, use combination of genetic algorithm (GA) and computational neural network (CNN) approaches to build a model incorporating the non-linearity between incidences and hypothesized risk factors. Here GA is used for risk factor (variable) selection and CNN for model building mainly because of their ability to capture complex relationships with higher accuracy than linear models. The second objective is to propose a method to measure the relative importance of the selected risk factors included in the model. The study is situated in the metropolitan area of Minnesota, which had experienced significant outbreaks from 2002 till present.