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Abstracts of Global Public Health 2015 (ISBN 978-955-4543-31-7)
West Nile Virus in Mosquitoes: Accurately Predicting Outbreaks with Data Science
Damian R. Mingle
Data Science, WPC Healthcare, United States of America
Objectives: This study intends to examine the effects that a data science approach may have regarding
the ability for a machine to predict when and where different species of mosquitoes will test positive for
West Nile virus given weather, location, testing, and spraying data
Methods: We analyzed weather data and geographical information system (GIS) data to predict whether
or not West Nile virus is present for a given time, location, and species. Algorithm selection, hyper
parameter optimization, feature engineering, and model blending were further explored to optimize
model accuracy.
Results: When evaluating area under the curve, we were able to achieve over 80% accuracy as opposed
to a random model of 50%.
Conclusions: This more accurate method of predicting outbreaks of West Nile virus in mosquitoes will
help cities and their departments of public health more efficiently and effectively allocate resources
towards preventing transmission of this potentially deadly virus. By making use of data science
techniques and methods in public health, health policies and preventive framework can be effectively
constructed to combat West Nile virus.
Keywords: west nile virus, data science, public health