A Nigerian-developed AI platform is shifting malaria surveillance from reactive reporting to real-time prediction. The system integrates epidemiological, climate, environmental, and geospatial data to identify transmission risks before outbreaks emerge.
It analyzes approximately 435,000 localized clusters, generating predictive risk maps. This highlights areas where disease may be under-reported or healthcare access is limited.
Public health officials can use these insights to stratify decisions. This includes targeting mosquito net distribution, vaccine deployment, surveillance team placement, and resource allocation for maximum impact.
The platform is designed as a clinical decision-support tool. It aims to embed AI into routine public health decision-making for frontline healthcare workers.
Nigeria is the first implementation site. There are plans for expansion into the Democratic Republic of the Congo. The technology could also be adapted for other diseases like Ebola and Lassa fever.
Success depends on usability and integration into existing health systems. It represents a major shift toward predictive disease intelligence.