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Predicting Volume of Mosquito Borne Diseases Using A.M.E.A. Sensor/Radio Network
Country:United States of America
Student(s):Miguel Jose Bueno, James Ervin, Anushka Jain, Owen Luo, and Aarnav Tendulkar
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
The A.M.E.A. (AI-Powered Mosquito Environmental Analyzer) Sensor/Radio Network
is an effective tool towards predicting volumes of mosquito borne diseases. In this study, data
based on light, temperature, and precipitation is fed into a ML model that then processes said
data, producing Mosquito Borne Disease, (MBD), predictions for that specific area and climate.
Will the data received from the A.M.E.A. Network be sufficient enough in creating accurate
predictions in a given location? Using a pair of computers and a transmitter (Raspberry Pi
Speaker/Kenwood TS-440) and receiver (Computer microphone/Kenwood R600) unit, each
connected to their respective radios, we can demonstrate the ability to transmit data using a novel
technique with Slow Scan Television (SSTV) images that can be fed into an ML Model that
processes and decodes the data and images to create a prediction on the volume of MBDs in a
given area. These MBD predictions may then be compared to GLOBE data in order to assess if
our MBD predictions are accurate based on a given location and climate. Our decision forest
model had an accuracy of 64.6%, and the neural network Google Teachable Machine
image-recognition ML model is able to predict the color of an SSTV image with 100% certainty.
Since our AMEA sensor/radio network proved to be extremely accurate, in the future, scientists
and public health professionals may better prepare for outbreaks in advance, limiting the amount
of cases and casualties from mosquito-borne diseases in communities.
Keywords: citizen science; machine learning model; mosquito prediction; sensor/radio network;
engineering
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