Benjamin is from Uganda and holds a BSc. with Education in Mathematics and Economics (2021) from Makerere University. He then pursued an MSc. in Mathematical Sciences at AIMS Rwanda (2023), where his thesis focused on ‘A Mathematical Model for Oncolytic Virus Spread.’

Benjamin recently completed a second MSc. in Mathematical Modelling at the University of L’Aquila, Italy, in September 2025, as part of an Erasmus Mundus program. His master’s thesis involved analysing and simulating ‘Vlasov-type Models for Nonlocal Interacting Agents.’

His primary research interest lies in applying mathematics to epidemiology, where it plays a crucial role in modelling and simulating infectious disease dynamics.

Outside of mathematics, Benjamin enjoys reading, listening to worship music, improving his cooking skills, and spending time in nature whenever he gets the chance.

Project title:
Mathematical Modelling of Tuberculosis-Influenza Interactions and the Impact of Influenza Vaccination on Tuberculosis Dynamics

Supervisor(s):
Ruth Bowness

Project description:
Tuberculosis (TB) remains a major global health challenge. Although tuberculosis can persist in the host for extended periods sometimes months or years, influenza is a short infection that usually lasts only a few days. However, growing evidence suggests that having the flu may weaken the body’s ability to control TB, potentially influencing whether a latent infection progresses to active disease. This project will develop a
mathematical modelling framework to investigate how short, seasonal flu infections may influence the longer-term progression and spread of TB. We will build models that connect what happens inside an infected person with what happens across a population, allowing us to explore whether short, seasonal flu outbreaks can have longer-lasting effects on TB.

The project will also investigate whether flu vaccination could indirectly affect TB outcomes by preventing or reducing flu-related disruptions to TB control. Using mathematical analysis and computer simulations, informed by available data on vaccination and TB, we will quantify how factors like vaccine timing, uptake, effectiveness, and how long protection lasts all interact with the different timescales of flu and TB. Ultimately, the work seeks to provide a mathematical framework to improve our understanding of how short-term infections can influence longer-lasting diseases, and to help assess whether interventions targeting one infection can generate broader health benefits for populations.