Evidence-based decision making for malaria elimination applying the Freedom From Infection statistical framework in...

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Modeling

Background:

• Routine surveillance is vital for malaria control programs, but its effectiveness in measuring disease burden is often limited.
• Inferences from routine data can be inaccurate if the surveillance system's sensitivity is not well understood.

Purpose of the Study:

• To extend the Freedom From Infection (FFI) framework for species-specific malaria surveillance.
• To estimate surveillance system sensitivity and probability of freedom from malaria.
• To integrate multiple surveillance components and apply the FFI model in malaria-eliminating settings.

Main Methods:

• Collected monthly routine data on Plasmodium falciparum and Plasmodium vivax from 1515 facilities across five countries.
• Incorporated data from community health workers and cross-sectional surveys (active case detection).
• Analyzed data using FFI models accounting for multiple malaria species and surveillance components.

Main Results:

• Strong surveillance systems require adequate testing/treatment supplies, trained personnel, and accessible facilities.
• Only half of facilities achieved sufficient sensitivity for malaria elimination using passive case detection alone or combined with active case detection.
• Species-specific sensitivity estimates were generated for Plasmodium falciparum and Plasmodium vivax.

Conclusions:

• The FFI model provides species-specific insights for malaria surveillance decision-making.
• Robust routine surveillance systems are sufficient for achieving and maintaining malaria freedom.
• Integrating community case management and active case detection improves confidence in elimination when routine data is insufficient.