Student Research Reports
Open social media sharing options menu
West Nile Virus: Relation to Population Density, Precipitation, and Elevation
Country:United States of America
Student(s):Diya Deepak, Ananya Gummadi, Asher Jiang, and Harry Wu
Grade Level:Secondary School (grades 9-12, ages 14-18)
GLOBE Teacher: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:
View Video
Presentation Poster:
View Document
Optional Badges:I am a Collaborator, I am a Data Scientist, I make an Impact
Language(s):English
Date Submitted:01/23/2023
Vector-borne diseases are spread to humans by a variety of arthropods. The Culex
mosquito stands out as one of the most potent transmitters of many diseases, such as the West
Nile Virus. Past efforts to map a correlation between population density and the rate of this
disease spread have mostly reported inconsistent results. Therefore, socioeconomic class and
type of human settlements must be taken into account in order to confidently establish a
correlation, particularly in areas that contain urban settlements. Despite past inconsistent results,
determining a correlation between environmental factors and the rate of spread of vector-borne
diseases is crucial for the safety of humans living in many regions around the world. This project
will focus first on West Nile Virus data from the state of Texas, before extending the methods
used to other regions and diseases. Using data obtained from various sources and a symbolic
regression tool in the form of HeuristicLab, this research project aims to find definitive
correlations between the amount of WNV cases and the population density, precipitation level,
elevation, and temperature of Texas counties. After obtaining the necessary data and running the
symbolic regression tool, the equation generated displayed a very strong correlation between
population density and WNV cases, and a much weaker correlation between WNV frequency,
precipitation levels, and elevation. There was no correlation with temperature. In the future, with
greater availability of West Nile Virus data, including non-environmental factors that affect an
individual’s susceptibility to this disease, more accurate predictions could be used to issue
warnings and mitigate the effects of WNV transmission. Additionally, by modifying the equation
to work with different programs and programming languages, it can be used in a similar fashion
to predict outbreaks and identify correlations between other diseases.
Keywords: West Nile Virus, symbolic regression, outbreak, population density