What does this research mean for the field?

Artificial intelligence, particularly machine learning and ensemble models, effectively predicts infectious disease outbreaks like malaria and COVID-19 in sub-Saharan Africa, but its widespread adoption is hindered by significant data, infrastructure, and skills gaps. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review explores how artificial intelligence can enhance infectious disease surveillance in sub-Saharan Africa.

May 30, 2026Open Access

Application of Artificial Intelligence in Infectious Disease Surveillance in sub-Saharan Africa: A Scoping Review

Key Points

This review explores how artificial intelligence can enhance infectious disease surveillance in sub-Saharan Africa.
Conducted a scoping review of AI applications in infectious disease surveillance.
Focused on machine learning techniques and their effectiveness.
Evaluated the challenges such as data and skills gaps affecting AI adoption.
Machine learning is the primary method used for AI surveillance.
AI effectively predicts outbreaks and identifies disease hotspots, particularly for malaria and COVID-19.
Ensemble models show better performance compared to single models in outbreak predictions.

Abstract

• Machine learning dominates AI surveillance in SSA • AI predicts outbreaks and identifies disease hotspots • Ensemble models outperform single-model approaches • Applications focus mainly on malaria and COVID-19 • Data, infrastructure, and skills gaps limit AI adoption

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Cite This Study

Makola et al. (Fri,) studied this question.

synapsesocial.com/papers/6a1a7ecb0307b7850943158e https://doi.org/https://doi.org/10.1016/j.smhl.2026.100676

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