Use Case Description
Mosquito-borne diseases such as dengue, malaria, and yellow fever account for a significant share of the global burden of vector-borne diseases, placing 80% of the world’s population at risk and causing approximately 700,000 deaths annually. These diseases are increasing in prevalence, geographical distribution, and severity, posing a growing threat to humans and animals worldwide. Another neglected but serious public health concern is leptospirosis, which affects around 1.03 million people globally each year. The disease is especially prevalent in tropical and subtropical regions, with the highest burden among low- and middle-income countries.
These zoonotic diseases often arise from complex interactions between vectors and hosts, which are, in turn, shaped by environmental factors such as temperature, rainfall, and land use. As these factors shift due to climate change and human activity, they can be mapped spatially and temporally to estimate human disease risk.
Earth Observation (EO) data, when combined with other environmental predictors and disease surveillance data, feeds into advanced modeling frameworks. These models are linked with climate forecasts to predict potential disease outbreaks. By identifying areas and times of elevated transmission risk, these tools support public health decision-making, enabling stakeholders to take timely, evidence-based actions to reduce disease burden. Additionally, they enhance disease surveillance through automated epidemiological bulletins, improving early warning systems and overall response capacity.