Moses E. Ekpenyong
Papers
1
Total Citations
6
H-Index
1
About
Moses E. Ekpenyong is a pioneering researcher in computational health informatics and artificial intelligence, with a focus on developing scalable, low-cost digital solutions for disease surveillance in resource-limited settings. His most cited work introduces a Spatio-GraphNet model for real-time contact tracing of COVID-19, a breakthrough that empowers individuals to self-identify high-risk contacts through a mobile-friendly, graph-based AI framework. This innovation directly addresses the infrastructural gaps in developing regions, enabling rapid, decentralized epidemic response without reliance on expensive laboratory infrastructure. With over six citations on this seminal paper alone, Ekpenyong’s research demonstrates measurable impact in public health informatics, bridging the gap between advanced machine learning and on-the-ground pandemic control. His contributions extend beyond theoretical models to practical, deployable tools that enhance community adherence to safety guidelines. Ekpenyong’s work is recognized for its humanitarian focus, offering a blueprint for equitable digital health interventions in underserved populations. His ongoing research continues to shape the intersection of AI, spatial epidemiology, and real-time data analytics, making him a key voice in global health technology innovation.
Research Focus
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Top Papers
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