Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.

Area of Science:

• Public Health
• Environmental Science
• Epidemiology

Background:

• Malaria Early Warning Systems (EWS) utilize environmental data to predict malaria risk and guide interventions.
• Despite their promise, EWS development and implementation face significant hurdles.

Purpose of the Study:

• To systematically review the evidence on malaria EWS, covering their settings, methods, performance, actions, and evaluation.
• To identify strengths, limitations, and areas for improvement in malaria EWS.

Main Methods:

• A comprehensive literature search was conducted across multiple databases and registers.
• 30 studies from 16 countries, encompassing diverse transmission settings and EWS purposes, were included.
• Data on EWS characteristics, outcomes, and experiences were extracted and synthesized.

Main Results:

• EWS employed various statistical and machine-learning models, with time series models being most common.
• Model performance was assessed using metrics like AIC, RMSE, and R-squared.
• Reported actions included vector control, case management, and health education, but evaluation of impact was limited by non-standardized methods.

Conclusions:

• Malaria EWS show promise but require methodological refinement and standardized evaluation metrics.
• Challenges include automating forecasting, ensuring scalability, and aligning predictions with public health actions.
• Future efforts must improve model precision, usability, and adaptability for enhanced malaria prevention and control.