active Part of EPIFORECAST Project Umbrella

Data-Driven Epidemic Forecasting

This project develops machine-learning enhanced epidemic models that fuse surveillance data, mobility patterns, and climate signals to produce real-time forecasts for outbreaks across West Africa.
EP

Data-Driven Epidemic Forecasting Project Umbrella

By combining mechanistic SEIR-style models with Bayesian inference and GPU-accelerated simulation, the project provides decision support tools for ministries of health and regional response teams.

Project Highlights

High-Performance Computing

Access to NIMS Ghana's cluster for intensive mathematical modeling and parallel processing research.

Industrial Partnerships

Direct collaboration with Ghana's leading energy and manufacturing sectors on live projects.

Advanced Algorithms

Deep dive into numerical analysis, optimization, and stochastic modeling techniques.

Global Impact

Project findings are published in international journals, ensuring global recognition for our researchers.

Affiliated Academic Programs

Scientific Computing & Industrial Modelling Data Science for Climate Change & Global Health
Research Areas

Stochastic epidemic modelling, Bayesian inference, machine learning, public health decision support