Burundi’s AI-Powered Mpox Surveillance Network
Photo Credit: Feepik
Overview
Mpox has re-emerged as a serious public health threat, with the Clade Ib strain driving an active outbreak across Burundi at a scale that strained existing surveillance and response systems. For frontline health workers operating in under-resourced districts, delayed case detection and fragmented epidemiological data translate directly into slower containment and preventable spread.
The AI4Mpox Burundi team responded by building a machine-learning-ready epidemiological dataset and piloting an AI-powered early warning dashboard across three health districts in Bujumbura. The approach integrates artificial intelligence with mathematical modelling to move disease surveillance from reactive to anticipatory equipping decision-makers with evidence they can act on before outbreaks escalate.
Early results from the pilot are demonstrating the value of faster, data-driven outbreak intelligence. By combining predictive modelling with ground-level epidemiological data, the project offers a replicable model for AI-supported public health response in resource-constrained settings.