Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

Area of Science:

• Climate science
• Epidemiology
• Public health

Background:

• Malaria epidemic control is a global health priority.
• Climate variability significantly influences malaria transmission in Africa by affecting mosquito and parasite dynamics.
• Seasonal climate forecasts offer potential for early warning of epidemic risk.

Purpose of the Study:

• To develop a malaria incidence forecasting system using seasonal climate predictions.
• To assess the system's effectiveness in predicting malaria risk in Botswana.
• To provide actionable insights for malaria control decision-making.

Main Methods:

• Utilized dynamically based, seasonal-timescale, multi-model ensemble climate predictions from European global coupled ocean-atmosphere models.
• Applied the forecast system to predict malaria risk in Botswana, a region with established climate-malaria links.
• Evaluated the system's probabilistic prediction skill and lead time compared to traditional methods.

Main Results:

• The developed forecast system successfully predicted malaria risk in Botswana.
• Achieved up to four months lead time for malaria warnings compared to those based on observed precipitation.
• Demonstrated a comparably high level of probabilistic prediction skill.

Conclusions:

• Seasonal climate forecasts can provide valuable early warnings for malaria epidemics in climate-sensitive regions.
• The developed forecasting system enhances malaria risk prediction, enabling better resource allocation for control efforts.
• Integrating climate-based forecasts into public health strategies can improve epidemic preparedness and response.