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This collection of resources explores ways in which mathematics can help to improve our understanding of the spread of diseases in our communities. The classroom activities included in this collection vary from small group activities to those which can be done by the whole class together, and most include an interactive activity which encourages learners to explore changes to their modelling assumptions.
Students are introduced to mathematical modelling, and asked to consider the strengths and weaknesses of different models, and the importance of carefully interpreting mathematical results. They are encouraged to explore more sophisticated models and consider the possible impact our everyday behaviour has on the transmission of infectious diseases.
Short video clips feature Professor Julia Gog OBE, a Professor of Mathematical Biology at the University of Cambridge, who uses mathematics to better understand how diseases spread through our population. In the clip below, Julia talks about her job as a Mathematical Modeller.
The resources fall into four parts, and a concluding section. They are designed to be used in a sequence of lessons - here is a lesson by lesson breakdown. Detailed "Teachers' Resources" are provided for each part, showing how each part builds on the previous one.
With just some simple arithmetic, Julia introduces us to a basic mathematical model of the spread of infectious diseases, and then introduces us to R, one of the most important numbers in disease modelling.
Julia introduces the 'Lucky Dip model', and the accompanying interactivity, which take into account the impact of herd immunity.
Julia refines our model, and the interactivity offers a chance to simulate the outbreak of an infectious disease, recognising that not everyone is the same.
Here we make the model more realistic, by exploring the effect of movement by a population on the spread of an infectious disease. There's an interactivity that allows you to party, commute, and visit friends.
Here we find out more about what life is like for a researcher like Julia.