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Malaria is a life threating disease that is caused by infected Anopheles mosquitos. In 2017, there were an estimated 219 million recorded instances in 87 countries, with an estimated 435 deaths. Guyana is South American LMIC countries where malaria is hyper and holo-endemic as a result of populations living either within or on the periphery of forest environments. It is estimated that 267,000 people (35% of the population) are within high transmission (>1 case per 1000 population) regions, but it is indigenous communities that bear the greatest burden of infectious diseases. According to the 2017 WHO data, in Guyana there were 24.9K estimated cases and 33 estimated deaths.

This project combines indicators of environmental and landscape condition building on the integrated analysis of remote sensing, air-borne and ground sensors to provide real-time, low cost and accessible decision-support. It proposes the implementation of a multiscalar approach to monitoring the environmental conditions that are early predictors of outburst of malaria epidemics. The approach relies on acquiring environmental data through (i) remote sensing analysis to provide regional data on surface water distribution, soil moisture, and vegetation health and biomass; (ii) the use of remotely-controlled drones for mid-level monitoring (e.g. distribution of surface water, phenology of plants); and (iii) the deployment of remotely controlled mobile sensing units (rover that is equipped with a range of sensors and sampling units which can be adaptively deployed depending on the remote sensing analysis) for ground-based environmental monitoring (e.g. water depth/temperature, mosquito density and species; wind speed/direction).