“Development of an ensemble deep learning model for predicting lassa fever outbreak”
Country of Study
Nigeria
Institution
Federal University of Technology
Expected Year of Completion
2024
Thematic Area
Information and Technology
Education
Iyanu is pursing a Computer Science PhD at the Federal University of Technology in Nigeria, and a Lecturer at Rufus Giwa Polytechnic, Owo. She is an MSc graduate in Computer Science from the University of the Witwatersrand and is scheduled to complete her studies in 2024.
Research Summary
Lassa fever outbreaks require accurate forecasts to enable public health authorities develop and implement interventions that will help to control and manage them effectively. Iyanu’s research is expected to predict future Lassa fever outbreaks, which are increasing in Africa using an ensemble deep learning model. To the best of our knowledge, no study has been conducted on prediction/forecast of Lassa fever outbreaks in Nigeria using machine learning or deep learning techniques. Predictive models have shown promising results based on their use for other common infectious diseases. Deep learning, itself a form of machine learning, will be investigated in this study due to the complex nature of the problem to be solved. When compared with machine learning, deep learning models allow computers to solve more complex problems even when the dataset is very diverse, unstructured or inter-connected. Policy-makers can also employ preventive measures and allocate sufficient resources in areas with the highest epidemic risk, when a warning alert/signal is provided early enough.
Publications:
1. Modelling and Predicting the Spread of Covid-19: A Continental Analysis
2. Facial micro-expression recognition: A machine learning approach
3. Solving school bus routing problem using genetic algorithm-based model
