Development of a Detection and Early Warning System for Malaria Risk in the Amazon


This project combines remote sensing, hydrologic forecasting, mosquito larvae mapping, and public health surveys to build a platform for malaria prevention and control in a highly endemic region of the Amazon.

Data on human settlement and activity are being collected to develop spatially-explicit models of malaria risk in the Amazon

Malaria is a leading cause of morbidity in Amazon-basin countries. Major challenges remain in targeting intervention and control strategies, particularly the distribution of health resources (treatments, diagnostics, and long-lasting impregnated nets) due to eco-social dynamics causing a disconnect between where people are infected and where they are diagnosed. This feasibility study builds on a collaboration between investigators who combine expertise in land use, climate and ecological modeling with epidemiological studies of vector-borne disease, biostatistics, and demography in order to inform health interventions.

Watering Hole
Ongoing studies of population and land-use effects of malaria risk are being conducted


The long-term objective of our study is to develop real-time, spatially-explicit models of malaria risk in the Amazon that improve the efficacy of targeted interventions and inform distribution of health resources. Our multidisciplinary team will examine the feasibility of this objective by demonstrating three aims in the Loreto Region of Peru: (1) assimilate multiple Earth Observations to drive spatially- explicit ecological models of Anopheline mosquito distribution, with focus on Anopheles darlingi, Anopheles oswaldoi, and other suspected primary vectors of malaria in the Amazon; (2) develop a Human Activity and Settlements map, which will use a spatially-explicit model of known locations of human settlements from census and regional studies, areas of forest concessions (for logging and oil production), and indicators of forest disturbance to identify permanent and temporary areas of human activity; and (3) integrate ecological and human population models from aims (1) and (2) using the Ross-MacDonald-Dietz mathematical malaria model framework to create spatial risk maps of human malaria risk. Development of each aim will be supported by data collected from exogenous sources: an extensive database of adult and larval anophelines; human demographic data from national censuses and surveys and from longitudinal cohort studies; monthly malaria cases reported to the Peruvian health ministry (MINSA); and ongoing studies of population and land-use effects on malaria risk.

This research is sponsored by the NASA Applied Sciences Program.


remote sensing, hydrologic forecasting, mosquito larvae, malaria, Amazon, Peru, deforestation

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