Artificial intelligence in African malaria control programs: opportunities and risks

Area of Science:

• Public Health
• Infectious Diseases
• Artificial Intelligence

Background:

• Malaria persists as a critical health issue in Sub-Saharan Africa, with conventional control methods facing significant challenges.
• Drug and insecticide resistance, climate variability, and health system weaknesses impede progress in malaria elimination efforts.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in strengthening malaria control programs across Africa.
• To highlight practical case studies and emerging AI methodologies for malaria surveillance, diagnostics, and outbreak prediction.

Main Methods:

• This narrative review synthesizes existing literature and case studies on AI implementation in African malaria control programs.
• It examines AI-driven bioinformatics, parasite modeling, and network analyses for understanding vector-parasite interactions.

Main Results:

• AI applications include drone-assisted vector surveillance (Kenya), real-time case reporting via mobile health (Nigeria), and climate-informed forecasting (Tanzania).
• AI also aids in genomic surveillance of Plasmodium parasites and modeling of parasite dynamics.

Conclusions:

• AI presents transformative potential for malaria control, offering enhanced surveillance, prediction, and resource allocation.
• Addressing challenges like data quality, ethics, and infrastructure is crucial for equitable and effective AI integration to accelerate malaria elimination in Africa.