Student Research Reports

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Mosquitoes are vectors for a number of serious illnesses, such as Dengue, Zika, Malaria, and West Nile Virus. In the United States, West Nile Virus (WNV) is the leading mosquito-borne disease (CDC 2022). As there are currently no vaccines to prevent WNV nor medications to cure it, government agencies must sustain financially taxing programs to monitor mosquito populations and WNV infections in an effort to prevent WNV outbreaks. In this study, we develop four machine learning models that forecast WNV infections in humans, enabling government and healthcare officials to take proactive action instead of reacting to real-time infection data. Our models take in data on ecological variables – such as humidity, wind, air quality, and vegetation — and use that data to predict future WNV infections five weeks in advance. We then present a comparative analysis of two types of machine learning models – support vector machine regressors and random forest regressors – to evaluate which is best suited for the task. Our results provide a streamlined solution for government agencies as they monitor WNV, enabling effective and low-cost preventative action. Keywords: West Nile virus, machine learning, disease prevention, epidemiological monitoring