AI and Mathematical Modelling for Mpox Response in DRC
Photo Credit: Feepik
Overview
Mpox continues to pose a severe public health threat in the Democratic Republic of Congo, where weak surveillance infrastructure, geographic remoteness, and strained health systems leave communities exposed. For the health workers and community members on the frontlines of outbreak response, the gap between emerging risk and timely action has long meant the difference between containment and crisis.
To close that gap, COSAMED developed AfiaGap, an AI-powered community surveillance platform that monitors Mpox alongside 12 other diseases in real time. Combining artificial intelligence with mathematical modelling, the platform supports evidence-based prevention and response by classifying disease risk with 98% accuracy, generating weekly epidemiological bulletins, and equipping health authorities with the data they need to act before outbreaks escalate.
Within its first year, AfiaGap extended coverage to more than 125,000 people and produced 35 weekly bulletins to guide outbreak response. Officially launched on World UHC Day in December 2025 with WHO support, the platform represents a scalable model for community-led, data-driven disease surveillance.