Sobre o instrutor:
Marwan Vaphy Sesay is a Liberian academic, researcher, and public health enthusiast with a strong background in economics, biostatistics, and quantitative research methods. He currently serves as a lecturer at the University of Liberia, where he teaches courses related to statistics, biostatistics, and quantitative methods in health research. His academic work focuses on strengthening students’ analytical and research skills, particularly in the application of statistical tools to public health and epidemiological studies.
Marwan’s research interests lie primarily in infectious disease epidemiology, including malaria, cholera, and Lassa fever. During the Ebola outbreak in West Africa, Marwan volunteered in response efforts, gaining practical experience in disease surveillance and public health emergency response. This experience strengthened his commitment to improving health systems and advancing research that supports evidence-based policy and disease prevention strategies.
Through his academic and professional pursuits, he aims to contribute to global health research and develop innovative analytical approaches to address major infectious disease challenges in Africa and beyond.
Resumo do seminário
This presentation explored 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 highlighted 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 presenter also discussed 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.
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