Predicting West Nile Virus (WNV) occurrences in North Dakota using data mining techniques

This paper discusses a model that predicts trap counts of Culex tarsalis, a female mosquito that is responsible for West Nile Virus (WNV) using machine-learning algorithms. Culex mosquitoes are the main transmission vectors for WNV infections. In this research, a Partial Least Square Regression (PLSR) has been deployed to predict mosquito trap counts of Culex tarsalis using historical meteorological and trap count data from 2005-2015. The associations between 10 years of mosquito capture data and the time lagged environmental quantities trap counts, rainfall, temperature, precipitation, and relative humidity were used to generate a predictive model for the population dynamics of this vector species. Statistical measure of Mean Absolute Error (MAE) is compared with other existing actual collected trap counts to analyze accuracy the predictive models. The paper also details the development of a user-friendly web-interface containing interactive web pages that allow users to visualize the North Dakota mosquito population, weather pattern, and WNV incidence data. The interface utilizes multi-layered Google Maps developed through Google Fusion Tables. An understanding of historical data and weather variables is essential for providing sufficient lead time to predict WNV occurrence, and for implementing disease control and prevention strategies such as spray period and hiring of seasonal mosquito workers. Further, an approach similar to the proposed approach of this paper, which involves the integration of data mining and data visualization techniques, brings novelty to vector control initiatives.