Explainers
JUNIPER is very pleased to collaborate with the fantastic team at Plus Magazine. Articles and podcasts produced by Rachel Thomas and Marianne Freiberger at Plus provide an explainer on the key mathematical modelling work done by JUNIPER members to a general audience. On this page you will find explainers to key epidemic modelling concepts as well as areas of current research. You can also see full details of all content produced with Plus.
Disease modelling for beginners
This collection of articles looks at some basic concepts in epidemiology and how mathematics plays a central role in understanding how infectious diseases spread.
Mind the data gap
Disruptions to public services are annoying – but that data about these disruptions is more useful than you might think.
Building bridges between modelling and policy
We meet some of the researchers who are trying to translate between the two very different worlds of mathematics and politics.
Adventures in Model Land
Would you like to play a game? Have an adventure? Join us on an adventure in Model Land!
What makes a modelling paper useful for policy?
Researchers, policy makers and communicators have distilled out some key principles for making mathematical research more useful for policy makers.
Liz Fearon: Co-producing mathematics with the public
Find out about a pioneering new project which builds mathematical models together with the people who are affected.
Co-production of mathematical models
Find out about a pioneering new project which builds mathematical models together with the people who are affected by them.
Keeping schools open in the next pandemic
To avoid full school closures in the next pandemic, or even epidemic, epidemiologists need crucial information from schools, students, and parents.
OK computer
There's a romantic vision of mathematicians only needing pen and paper for their work. Here's why this is far from the truth when it comes to mathematical modelling, used to solve problems in the real world.
Stochastic spread
When a new infectious disease enters a population everything depends on who catches it — superspreaders or people with few contacts who don't pass it on. We investigate the stochastic nature of the early stages of an outbreak.