Project Team: Brazil Health and Air Quality Team
Team Location: Jet Propulsion Laboratory, Pasadena, California

Each plot displays the epidemic potential (EP) value driven by temperature and humidity data for that year, and the total number of outbreaks for each year in the state of SÌ£o Paulo during the study period.

Authors:
Lorena Lopez (California State University, Northridge)
Scott Barron (University of California, Los Angeles)
Elise Lorenzana (University of California, Los Angeles)

Mentors/Advisors:
Ben Holt (DEVELOP Mentor, Jet Propulsion Laboratory)
Erika Podest (Jet Propulsion Laboratory)
Darren Drewry (Jet Propulsion Laboratory)

Abstract:

Vector-borne diseases claim millions of lives annually, particularly impacting children in underdeveloped and developing countries. According to the World Health Organization (WHO), dengue fever, along with dengue hemorrhagic fever (DHF), is the world’s fastest growing vector-borne disease. The ability to predict and mitigate the effects of dengue outbreaks has become increasingly difficult, as global climate change requires a complex understanding of shifts in local environmental systems. In order to reduce the impact of vector-borne diseases, including dengue, it is imperative to institute surveillance protocols to enhance timely mitigation in regions with limited resources.

Geographic Information Systems (GIS) and remote-sensing techniques can be utilized to characterize mosquito-breeding habitats and, thus, forecast vector-borne disease risk. This project aimed to provide an understanding of the potential locations prone to the spread of mosquito populations by utilizing satellite remote-sensing data to obtain information regarding vegetation, temperature, humidity, surface inundation, and precipitation. This project focused on the efficiency of vector-borne disease transmission by investigating the mean number of potential contacts infected by a mosquito population per infectious person per unit time (epidemic potential) of Aedes aegypti (primary vector for dengue) using temperature, humidity, precipitation, and surface inundation data along with human population density to identify areas of risk.

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