A week of modeling infectious diseases at MASHA: More than just equations

There is something unusual about learning infectious disease modelling in a room where everyone sees disease from a different perspective.

That was the setting for MASHA’s 2026 Mathematical Modelling of Infectious Diseases (MMID) short course, which brought together 17 participants at the University of Cape Town for a week of learning, discussion and hands-on modelling.

Participants represented disciplines including medicine, epidemiology, statistics, mathematics, climate science, health economics and public health, including MSc and PhD students and postdoctoral researchers. They came from different parts of South Africa and other African countries, bringing diverse experiences and perspectives on infectious disease research.

Some were already using models in their research, while others were more familiar with statistics and data and relatively new to infectious disease modelling. 

“That diversity became one of the week’s most valuable parts. A mathematical question could lead to a public health discussion, while a modelling assumption could be questioned from a clinical or epidemiological perspective,” said Kounoumi Sara Medekon.

When a simple model starts becoming real

Led by Prof Sheetal Silal, the course started with the foundations of infectious disease modelling, beginning with the SIR compartmental model. Participants then explored how models can be extended to better reflect real disease transmission, including asymptomatic infections, immunity, spatial differences, seasonality and climate.

With laptops open and R running, participants worked through practical exercises alongside the lectures, turning theoretical concepts into models they could build, test and explore themselves.

One of the highlights was the group work. Participants developed models for different infectious diseases, including gonorrhoea, tuberculosis and COVID-19, and presented their work to the class. The presentations sparked lively discussions, with Prof Silal and other participants questioning assumptions, suggesting alternative approaches and offering feedback.

The course also went beyond disease transmission. Dr Rachel Hounsell introduced participants to health economic evaluation. Participants considered not only whether an intervention could reduce infections or deaths, but also the resources required to implement it, connecting the modelling exercises to decisions made in real public health settings.

A good model is not the end of the story

As participants became more comfortable building and extending their models, the course moved on to what happens when those models meet real-world data. They learned how to fit models to data and use sensitivity analysis to understand how uncertainty can influence results. They also explored how interventions such as vaccination can be represented and how spatial differences, seasonality and climate can shape disease transmission.

The focus then shifted to how participants interpret and communicate modelling results. Ethics, prediction, reporting and data visualisation were discussed before participants put some of these ideas into practice with R Shiny, building an interactive application that made model results easier to visualise, explore and communicate.

What we took away

By the end of the week, participants had done much more than attend lectures. They had built their own models, discussed and defended their assumptions, received feedback and learned from people working in different disciplines. Between the R code and technical discussions, there was plenty of group work, questions, laughter, and opportunities to connect.

A key strength of MMID 2026 was the diversity of perspectives it brought into the room. By combining mathematical, clinical, epidemiological and public health perspectives, the course demonstrated how collaborative modelling can help address complex infectious disease challenges and support evidence-informed public health decision-making.