In 1911, Christophers developed an early-warning system for malaria epidemics in Punjab based on rainfall, fever-related deaths and wheat prices. Since that initial system, researchers and practitioners have continued to search for determinants of spatial and temporal variability of malaria to improve systems for forecasting disease burden. Malaria thrives in poor tropical and subtropical countries where resources are limited. Accurate disease prediction and early warning of increased disease burden can provide public health and clinical health services with the information needed to implement targeted approaches for malaria control and prevention that make effective use of limited resources. Malaria forecasting models do not typically consider clinical predictors, such as type of antimalarial treatment, in the forecasting models.
Objective
The objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources.