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How does maths help in tackling infectious diseases? Join Julia Gog to find out in this series of videos and articles, and have a go at modelling diseases yourself!

With just some simple arithmetic, you can build a basic mathematical model of how a disease might spread. Julia Gog explains how, and there's also some Lego action...

You can explore how we might extend our model but running your own epidemic with our Lucky Dip interactivity. Follow along with Julia as she paves the way to a model that is very similar to the mathematics disease modellers use every day.

In Part 3 Julia refines our model to use one of the most important numbers in disease modelling. And there's a chance for you to explore its meaning using a new interactivity.

In the final Part we explore what other aspects we need to consider to make a model more realistic. There's an interactivity that allows you to party, commute, and visit friends and we find out more about what life as a research is like from Julia.

In this final part, you can meet the researchers themselves and find out about the real research questions that Julia and some of her colleagues are working on!

"Find problems you enjoy, and just work on them. It doesn't matter if you never solve them." We speak to Andrew Wiles on the 30th anniversary of his announcing the proof of Fermat's Last Theorem.

A mathematical, and personal, look into how we all had to balance the different harms of the virus and the steps we took against it.

We love talking to young researchers and laureates at the Heidelberg Laureate Forum, and this year we produced a daily video diary to take everyone behind the scenes of this special digital HLF.

In this short video Clement Twumasi tells us about his work helping to understand a parasite that has been decimating fish populations around he world.

In this short video Briane Paul Samson tells us about his work developing intelligent algorithms to help us navigate on busy roads.

In this short video Yasmeen George talks to us about her work using machine learning to help diagnose disease from medical images.