On Your Mark: How Mathematical Modelling Is Helping Uganda Stay Ahead of Disease

For many people, pure mathematics ends in the classroom.

Differentiation, integration and mathematical equations often feel disconnected from everyday life, leaving students wondering whether years spent mastering mathematical concepts will ever matter beyond an exam.

At the African Centre of Excellence in Bioinformatics and Data-Intensive Sciences (ACE-Uganda), those same mathematical principles are helping answer some of the most urgent questions in public health.

Somewhere in the world right now, an outbreak is unfolding, a tumour is evolving, and a body is responding in ways we don't fully understand yet. Our disease modelling working group is using data, computational and mathematical approaches to better understand these complex processes and inform preparedness and response.
The rapid growth of large-scale datasets, real-time health information, and global connectivity has made analyses possible that once seemed like science fiction. AI and mathematical models can now combine vast amounts of information to help us explore what might happen next and assess different possible scenarios. The same fundamental principles underpin models used to forecast weather, anticipate demand, recommend what we buy, and shape what we see on social media.

At ACE-Uganda, our disease modelling working group applies these principles to health, using mathematics, data and computational infrastructure to understand how diseases spread and evolve, identify where transmission may increase, and explore which interventions could make the greatest difference. This includes both infectious diseases that can emerge and spread rapidly and non-communicable diseases whose effects unfold over years.
So how does a working group like this actually come together? Members begin by developing their own research questions, which are presented back to the group; the strongest are taken forward as active projects, and other members join those teams rather than starting alone. Each team reports back periodically, presenting progress for review, comments, and guidance from mentors who steer the work across both infectious and non-communicable diseases.

Currently, one subset of the group is modelling how an active outbreak moves differently through different kinds of communities, work that could reshape where and how interventions are deployed on the ground. Another subset looks backward to look forward, studying decades of a recurring disease's history, its patterns of emergence, decline, and return, to help policymakers and preparedness teams get ahead of the next one instead of reacting to it.
Other work traces how a fast-moving viral disease shifts across age groups and across borders, insight that could shape vaccination strategy and readiness in countries that haven't yet felt its full reach. Work like this can be the difference between an outbreak contained within weeks and one that drags on for months, and between a vaccination campaign that reaches the right people first and one that reaches them too late. On the non-communicable side, part of the team model how a single cancer diverges into subtypes that behave, and must be treated, differently, work that could eventually change how clinicians approach diagnosis and care.

Different diseases, different questions, but the same underlying principle: that with enough data, the right models, right skills and access to the right computational infrastructure, we can help scientists move from reacting to anticipating it.
This disease modelling working group brings together statisticians, bioinformaticians, mathematicians, clinicians, data scientists, physicists, public health specialists, laboratory technologists, economists, graduates, interns, master's and PhD fellows, and postdoctoral researchers. Together, they go back to the same basic training, relearning it, sharpening it, and pointing it at something that matters: the chance to face the future a little steadier, in the one domain where being steadier saves lives.

We've been handed a rare set of tools: mathematics, skills, computing power, and unprecedented volumes of genomic, clinical and population-level data. We can't afford to let that sit unused while outbreaks catch us unprepared, while patterns we could have seen coming find us looking the other way.

So: on your mark.

The team is already at the line. Over the coming weeks, we'll take you inside the disease modelling working group to explore the work, models, outbreaks, and questions we're answering. Stay with us. This is only the beginning.