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Forecasting West Nile Virus Infections: A Machine-Learning Approach to Epidemiological Monitoring
Country:United States of America
Student(s):Rachel M. Chen, Aidan P. Schneider, Francisco E. Rodriguez, Starlika Bauskar
Grade Level:Secondary School (grades 9-12, ages 14-18)
GLOBE Educator(s):Cassie Soeffing
Contributors:Dr. Rusty Low, IGES, scientist
Peder Nelson, OSU, sme
Dr. Erika Podest, NASA JPL, scientist
Andrew Clark, IGES, EO Researcher and Data Analyst
Report Type(s):International Virtual Science Symposium Report, Mission Mosquito Report
Protocols:Earth As a System, Mosquitoes
Presentation Video:
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Presentation Poster:
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Language(s):English
Date Submitted:01/24/2023
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