US-based Nigerian researcher creates AI tool to detect outbreaks early

US-based Nigerian researcher Oluwatobi Akinbode has developed PRISM-EW™, an AI-powered tool that analyses surveillance data to give early warning of infectious disease outbreaks.

United States-based Nigerian researcher Oluwatobi Akinbode has developed an artificial intelligence-powered tool to improve early detection of infectious disease outbreaks, PUNCH Healthwise reports.

The tool, PRISM-EW™ (Probabilistic Rt-Integrated Surveillance Model for Early Warning), uses AI and statistical modelling to analyse surveillance data, estimate transmission trends and flag possible outbreaks.

Central to it is an Outbreak Pressure Index, which scores transmission trends, disease burden, abnormal case patterns and forecast risks to classify threats by severity.

“Public-health surveillance should go beyond documenting disease outbreaks after they occur. We need intelligent systems that can recognise early warning signals, quantify uncertainty, and support timely intervention,” Akinbode said.

He said the tool is designed for public-health agencies and researchers tackling diseases such as influenza, COVID-19, measles and cholera.

“Artificial intelligence becomes valuable in healthcare when its predictions are interpretable, scientifically grounded, and useful to the people making critical decisions,” he added.

Akinbode’s previous work includes HIV-risk research at Duke University and AI engineering at LexisNexis.