October 21st, 2025. Tuesday. TG23. 3PM.
Speaker: Abimbola Afolayan (Middlesex Univerisity, London)
Title: Applying Machine Learning to Investigate Malaria Risk Factors and Predict Malaria incidence in Southwest Nigeria
Abstract: The malaria epidemic has emerged in recent years as one of the most challenging issues in Nigeria where climate factors are favourable to species of mosquitoes transmitting the malaria parasite. Despite initiatives to lower malaria incidences and deaths in Nigeria through various WHO-recommended malaria intervention strategies, Nigeria is still responsible for around 31.9 percent of all malaria deaths worldwide. Hence, there is a dire need to address the impacts of climate change on malaria in Nigeria and develop a cutting-edge solution that can enhance decision-making in the health sector. Therefore, this study will investigate the climatic factors that affect the incidence of malaria, identify the risk factors associated with malaria infection
transmission, design a risk maps that will highlight locations with the highest malaria risk, predict the malaria incidence based on climate variability using weighted average ensemble learning that combines CNN, LSTM, and GRU, evaluate the performance and effectiveness of the model developed and deploy the model designed on android mobile app, web interface and software. The study area is Southwest, Nigeria. The weighted average ensemble learning model will be based on the malaria incidence data and climatic data. The malaria incidence data will be collected from primary health centres in Southwest, Nigeria, West Africa. The significance of this proposed study cannot be overemphasized. The proposed study will help combat the menace of malaria (a public health problem) in Nigeria and other endemic regions of the world.
Bio: Dr. Abimbola Helen Afolayan is a UK AREF Postdoctoral Fellow sponsored by the United Kingdom African Research Excellence Fund (UK AREF) at the Department of Computer
Science, Middlesex University, London, under the supervision of Dr. Olugbenga Oluwagbemi at Middlesex. She earned her PhD degree in Computer Science from the Federal University of Technology, Akure (FUTA), Nigeria, in 2019, where she currently lectures in the Department of Information Systems. Her research applies artificial intelligence and machine learning to biomedical and public health challenges, with a particular focus on malaria prediction and risk mapping in Southwestern Nigeria. She has authored twenty-eight (28) peer-reviewed journal and
conference articles, presented at local and international conferences. She has also contributed to major funded projects, including a Nigerian Tertiary Education Trust Fund, National
Research Fund (TETFUND NRF) grant that produced a patented autonomous contact-tracing wearable device. Dr. Afolayan has received some prestigious grants and fellowships, including the ACM-W Scholarship, ACM SIGCOMM Grant, and selection for the 2019 Heidelberg Laureate Forum.
She is an active member of some professional bodies such as the Computer Professionals Registration Council of Nigeria (CPN), the Nigeria Computer Society (NCS), the Organization
of Women in Science for the Developing World (OWSD), and Women in High-Performance Computing (WHPC). Her work reflects a strong commitment to advancing data-driven health
solutions and positions her as a rising voice at the intersection of computer science and global health.