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Forecasting West Nile Virus Infections A Machine Learning Approach to Epidemiological Monitoring
  • +1
  • Rachel Chen,
  • Aidan Schneider,
  • Starlika Bauskar,
  • Francisco Rodriguez
Rachel Chen
NASA STEM Enhancement in Earth Science
Aidan Schneider
NASA STEM Enhancement in Earth Science

Corresponding Author:aidan.p.sch@gmail.com

Author Profile
Starlika Bauskar
NASA STEM Enhancement in Earth Science
Francisco Rodriguez
NASA STEM Enhancement in Earth Science


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. 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 and share this data across various departments 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 open-access data describing ecological variables – such as temperature, humidity, wind, air quality index (AQI), and enhanced vegetation index (EVI) — and use that data to predict future WNV infections five weeks in advance. We then perform a comparative analysis of the two types of machine learning architectures – support vector machine (SVM) regressors and random forest (RF) regressors – represented across our four models to evaluate which is best suited for the task. Our results indicate RF regressors are best suited to the task of forecasting WNV infections; however, SVM regressors perform comparably well and even exceed RF regressors when the magnitude of error is unweighted. Additionally, our results contribute a new perspective on the usefulness of AQI and wind speed for predicting mosquito-borne infections. Our RF regressor’s feature importance results indicate that AQI and wind speed were of similar importance as EVI and humidity – ecological variables well-known to influence mosquito population dynamics. Our work provides valuable directions for future research and development of early warning systems for disease prevention efforts as our models’ ability to forecast WNV infections five weeks in advance provides critical lead time for government officials to pursue mosquito containment efforts and healthcare facilities to increase capacity, enabling proactive action in combating WNV.
02 Jul 2023Submitted to ESS Open Archive
03 Jul 2023Published in ESS Open Archive