Visualizing and Predicting Culex Tarsalis Trapcounts for West Nile Virus (WNV) Disease Incidence using Machine Learning Models

This paper discusses how visualization and machine learning models can be effectively used to track and forecast trap counts of Culex Tarsalis, female mosquitoes responsible for spreading the West Nile Virus (WNV). This paper applies four different machine learning models namely, Support vector machines (SVM), Regression tree (RT), Partial Least Square Regression (PLSR), and a hybrid combination of SVM-PLSR to multi-year WNV data sets. Precisely, historical data sets ranging from 2005-2015 was used to predict trap counts for 2016. This paper also discusses a tree-based data visualization technique for displaying historical trap counts. The visualization model is designed to focus on identifying trends in the behavior of Culex tarsalis by tracking parameters such as meteorological data, dead birds, WNV cases, human cases, and deaths. The preliminary results indicate that SVM model outperforms interms of accuracy than other machine learning models. Keywords: Culex Tarsalis, Partial Least Squares Regression (PLSR), Support Vector Machines(SVM), SVM-PLSR, West Nile Virus (WNV), Vector Control.