An Early Warning System for Vector-borne Disease Risk in the Amazon




  • Ernesto Ortiz

Start Date:

End Date:

  • Ongoing

An Early Warning System for Vector-borne Disease Risk in the Amazon

The Amazon basin is a region with significant vector borne disease burden. Malaria, leishmaniasis, leptospirosis, and others continue to disproportionately threaten the most remote and impoverished Amazonian communities where local response capabilities are limited and largely dependent on regional / national resources. The dynamics of these diseases are, in part, a function of physical environment; thus, hydrometeorology, landscape morphology, and land cover all contribute to whether vector species involved in disease transmission can thrive. Human infection risk is further modulated by settlement and activity patterns. Risk monitoring and early warning systems (EWS) are most effective when they consider both human and environmental processes. Under Feasibility Study funding from the NASA Applied Sciences Health and Air Quality Program (2011-2013), proposal team members developed a pilot malaria EWS for the northern Peruvian Amazon. We found malaria risk can be predicted using statistical methods that combine data from an advanced NASA land data assimilation system, satellite-derived land cover, and regional human population and malaria surveillance. Building on our pilot, we propose to: (1) operationalize our EWS to a larger geographic area, namely across the Ecuadorian border and the Southern Peruvian Amazon, employing finer-scale risk estimates; and (2) expand and evaluate system performance in predicting spatio-temporal variation of additional vector-borne disease endpoints, focusing initially on leishmania We will achieve these two goals by leveraging existing partnerships with the U.S. Naval Medical Research Unit No. 6 and the Peruvian and Ecuadorian Ministries of Health. The proposed system-146's innovation is that it will leverage data from new NASA missions (GPM, SMAP, VIIRS, Landsat8) and improved human settlement data to improve model prediction from the pilot study. Our spatio-temporal risk maps will bolster the abilities of our end users to prevent vector-borne disease outbreaks in the face of changing environmental and climatic conditions.

Last updated on January 10, 2018