Dengue Bites: Predicting Dengue Risk in Puerto Rico

EarthzineAssessing Human Risk, DEVELOP 2015 Fall VPS, DEVELOP Virtual Poster Session, Original

Predicted dengue risk for Puerto Rico in September 2009. Warmer colors indicate higher risk, and cooler colors indicate lower risk. Red dots indicate Confirmed Dengue Fever Cases. Image Credit: Puerto Rico Health and Air Quality Team

Predicted dengue risk for Puerto Rico in September 2009. Warmer colors indicate higher risk, and cooler colors indicate lower risk. Red dots indicate Confirmed Dengue Fever Cases. Image Credit: Puerto Rico Health and Air Quality Team

This is a part of the 2015 Fall VPS. For more VPS articles, click here

Category: Assessing Human Risk

Project Team: Puerto Rico Health & Air Quality

Team Location: NASA Ames Research Center ‰ÛÒ Mountain View, California

Authors:

Andrew Nguyen

Alannah Johansen

Martha Sayre

Mentors/Advisors:

Dr. Juan Torres-PÌ©rez (Bay Area Environmental Research Institute)

Abstract:

The dengue virus is the fastest-growing vector-borne disease in the world and has been declared endemic in the Caribbean. This deleterious illness is transmitted by tropical mosquitoes and can lead to hemorrhagic fever, shock, and death in severe cases, posing a major threat to the health of Caribbean communities. A high occurrence of the primary vector of the dengue virus (Aedes aegypti) has been detected in the city of San Juan, contributing to several dengue outbreaks, including instances in 2010, 2012, and 2013. This study examined environmental conditions contributing to Confirmed Dengue Fever Cases (CDFC) from January 2009-December 2013 using monthly NASA Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 0.5-degree land surface temperature (LST) products, 1 km Geostationary Operational Environmental Satellite system Puerto Rico Water Energy Balance (GOES-PRWEB) humidity products, 0.5-degree Climate Hazards Group InfraRed Precipitation and Satellite (CHIRPS) total precipitation (TP) modeled data, elevation, and land cover. These data were incorporated into a maximum entropy species distribution model to spatially delineate potential dengue risk, and output the statistical contribution of variables based on reported cases in Puerto Rico in monthly time steps. Additionally, the statistically significant variables were seasonally compared to CDFC from 2009-2013. Lastly, using Time Series Frequency Analysis, the correlation between MODIS 4 km sea surface temperature (SST) products and environmental conditions were tested to better understand the relationship between oceanic and land conditions contributing to dengue. Results indicate a strong significance of urban density, elevation, and TP. SST and environmental conditions correlation coefficients indicate moderate to strong relationships.

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