AMMnet webinar: Machine Learning Based Modeling of Malaria-A Systematic Review
The Applied Malaria Modeling Network (AMMnet) invites you to join the interactive webinar: Machine Learning Based Modeling of Malaria: A Systematic Review, to explore how machine learning is transforming the way we anticipate and respond to global health challenges.
Date: 7 April 2026
Time: 15h 00UTC
Location: Online
This presentation explores how machine learning techniques are being used to improve the prediction and detection of malaria outbreaks. Drawing on a systematic review of research published between 2013 and 2023, the session highlights how models such as artificial neural networks, decision trees, random forests, and support vector machines are applied to analyze environmental and socio-economic factors including temperature, rainfall, and population density.
These approaches have demonstrated diagnostic accuracies ranging from 80–95%, showing significant promise for forecasting outbreaks before they occur. The presentation will also discuss the strengths of these models, such as their predictive capability, as well as key challenges including data requirements and limitations in applying models across different settings.
Speaker: Marwan Vaphy Sesay, University of Liberia.Â
Click here to register.Â
