We design mathematical and computational frameworks — agent-based models, network science, Bayesian inference — to understand how infectious diseases spread, and to inform how they can be prevented, detected and controlled.
The broad themes our group works across, from vector-borne parasites to the computational tools used to model them.
Current Malaria Burden in Burundi (2022). Malaria remains Burundi's leading public health challenge, with over 8.2 million cases reported in 2022, up from 6.7 million...
Burundi faces a high burden of zoonotic diseases, particularly those linked to poor sanitation, close human-animal interactions, and limited veterinary infrastructure. These conditions...
Burundi faces critical research and response gaps regarding emerging infectious diseases. Surveillance systems remain fragmented, with weak integration across human, animal,...
Burundi's pandemic preparedness is significantly constrained by a range of systemic and research-related gaps, undermining its ability to detect, prevent, and respond to...
Non-communicable diseases (NCDs) in Burundi are rising at an alarming rate. Despite contributing to 37% of deaths nationally, research and policy responses...
Diagnostic research in Burundi reveals widespread gaps across disease categories, infrastructure, and access. These challenges compromise timely and accurate detection of both communicable and...
The platforms we lead and the competitively funded projects currently supporting our work. Full grant details and funding amounts are on our Grants & Awards page.
A regional platform advancing mathematical modelling, computational epidemiology and AI-driven decision support, coordinating multidisciplinary research and capacity strengthening in epidemic analytics across Central Africa.
Interdisciplinary research programme in mathematical modelling, scientific computing and AI for public health, coordinating international collaborations and PhD/MSc training.
Capacity building and methodological research in mathematical modelling and epidemic analytics across Central Africa.
Mathematical modelling, optimization and AI-enabled decision-support methodologies for mpox control in the Democratic Republic of the Congo.
Mathematical modelling, computational analytics and decision-support for mpox outbreak preparedness and response.
Scientific AI and predictive modelling for epidemic intelligence and surveillance.
We welcome partnerships with public health institutions, universities and funders across the region and beyond.