Harnessing data and satellites for early malaria warning: a global health imperative

Abstract

In light of the profound global health impact of pandemics, the reliance on data-driven insights to understand disease outbreaks has never been more crucial. Malaria is a disease transmitted by mosquitoes that is endemic to specific regions and causes severe illness and death to millions each year. The sensitivity of mosquito vectors to environmental factors like temperature, precipitation, and humidity enables the mapping of areas at high risk of disease outbreaks through satellite remote sensing. This study proposes the development of a practical geospatial system that can provide early warning for malaria. It combines Geographic Information System (GIS) tools, Artificial Neural Networks (ANN) for efficient pattern recognition, robust on-ground environmental data (including epidemiological and vector ecology data), and the capabilities of satellite remote sensing. The study employs Vegetation Health Indices (VHI) derived from satellite-mounted Advanced Very High-Resolution Radiometers (AVHRR) on a weekly basis with a 4-km resolution to predict malaria risk in Bangladesh. While the focus is on Bangladesh due to its significant malaria threat, the technology developed can be adapted for use in other countries and against different disease threats. Implementing an early malaria warning system would be a significant asset to global public health efforts. It would enable targeted resource allocation for pandemic containment and serve as a vital decision-making tool for national security assessments and potential troop deployments in disease-prone regions.