Bio
Oladimeji Samuel Sowole is a researcher specializing in epidemiology and mathematical modelling of infectious diseases. His work focuses on developing advanced network-based and geometric methods, including Forman-Ricci curvature and graph neural networks, to analyze epidemic dynamics and improve outbreak forecasting. He integrates computational modelling with real-world data to inform effective disease control strategies, with applications in vaccination prioritization and transmission prediction.
Educational Journey
African Institute for Mathematical Sciences
Master's of Science in Mathematical Sciences in Mathematical Sciences • Mathematical Sciences
2019Lagos State University
Bachelor of Science in Mathematics in Mathematics • Mathematics
2015Experience
African Institute for Mathematical Sciences
2025 - PresentDoctoral Researcher
2024 - PresentAfrican Institute for Mathematical Sciences
2020 - PresentEditors Role
Reviewer
Global Journal of Science Frontier Research
2020 - PresentResearch
Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches
In response to the global COVID-19 pandemic, this work aims to understand the early time evolution and the spread of the disease outbreak with a data driven approach. To this effect, we applied Susceptible- Infective- Recovered/Removed (SIR) epidemiological model on the disease. Additionally, we used the Machine Learning linear regression model on the historical COVID-19 data to predict the earlier stage of the disease. The evolution of the disease spread with the Mathematical SIR model and Machine Learning regression model for time series forecasting of the COVID-19 data without, and with lags and trends, was able to capture the early spread of the disease. Consequently, we suggest that if using a more advanced epidemiological model, and sophisticated machine learning regression models on the COVID-19 data, we can understand, as well as predict the long time evolution of the disease spread.
