Mission Epidemic: Data Education for Schools

Download a free role-based data science resource for pupils aged 14 - 17, developed from real research at the University of Edinburgh's Roslin Institute.

Step into the role of a research scientist: can your team use real epidemiological data to stop a disease outbreak before it spreads?

“Mission Epidemic” is a hands-on, role-based data science resource for pupils aged 14 to 17, developed from real research at the University of Edinburgh's Roslin Institute. This interdisciplinary resource has Curriculum for Excellence links with data analysis, topical science, genetics, and sustainable development, and also connects with literacy and numeracy across learning.

Pupils work in teams to clean and analyse simulated livestock datasets, plot findings, and present evidence-based disease control strategies, mirroring what scientists actually do.

What does the resource include?

  • Teacher's Guide (38 pages) covering scientific context, setup, facilitation notes, and reflection prompts
  • Teacher Slides (29 slides) to introduce the epidemic scenario and guide the session
  • Student Slides (33 slides) with the scientific background pupils need to engage with the data
  • Four distinct Excel datasets (Scenarios 1–4) for data analysis activities
  • Student Worksheet for documenting and reflecting on findings
  • A Glossary and Glossary Hunt activities across the material
  • Role-based printable ID Badges to assign research team roles, linked to Skills Development Scotland’s My World of Work

What are the aims of this resource?

  • Give young learners the opportunity to engage with real-life data science and epidemiological research
  • Show how mathematics, biology, and technology intersect to solve real-world problems in agriculture and public health
  • Provide access to current scientific research
  • Promote awareness of and inspiration around STEM subjects and careers in STEM, including data science, genetics, and veterinary research
  • Support teachers in delivering data literacy as a practical, applied skill

What are the intended learning outcomes of this resource?

Through bringing this resource into your classroom, your learners will have the opportunity to see how data science shapes decisions in the real world:

  • Identifying how data analysis and genetics can be applied to manage disease in agriculture and support sustainable development
  • Analysing simulated real-world datasets to draw conclusions and make evidence-based decisions
  • Explaining how genetics can influence disease resistance in animals and how selective breeding supports better health outcomes
  • Exercising critical thinking and problem-solving skills through collaborative team roles
  • Strengthening communication skills by preparing and presenting findings to their peers
  • Exploring career opportunities through role-based participation aligned with Skills Development Scotland's My World of Work framework

A short interview and activity explanation from Dr Smaragda Tsairidou a lecturer in Data Driven Innovation and Genetics 

Short interview with Dr Smaragda Tsairidou

Hi, I'm Smaragda and I'm a lecturer in Data Driven Innovation and Genetics at the Global Academy of Agriculture and Food Systems here at the University of Edinburgh. I'm a vet by training, so I studied veterinary medicine and I worked with farm animals, and then I did my MSc and PhD in Quantitative Genetics. I studied the genetic background of resistance to diseases in livestock. I worked in genetic epidemiological modelling to study the infectious disease spread and design strategies for disease control. I've also worked in aquaculture. As part of this, I worked a lot in developing my skills in bioinformatics, and I've worked a lot in computer programming and data analysis. If we think about infectious diseases, they are caused by pathogens and they can have a huge impact on human health and on animal health and welfare. Some diseases can be transmitted from animals to humans and that is a zoonotic transmission. We have a toolbox full of alternative possibilities and methodologies and strategies for disease control. One of the approaches that we can use is genetics. Through selective breeding, we can improve resistance to infectious diseases and reduce the spread of the diseases in the population. But we need data. So data is the source of information and thorough recording systems are really useful when it comes to disease control and monitoring of infectious diseases. But datasets can be huge, and therefore we need some bioinformatics and some computer based analysis in order to be able to analyse this data and extract some useful conclusions. And we need those conclusions in order to inform, for example, decisions in terms of, infectious disease control strategies and inform decision making in terms of policy decisions. The kind of data that I'm talking about could be, for example, the infectious status of an animal. That could be the outcome of a diagnostic test, for example, and also we could have some genotypes. So what we're going to look at this activity is simulated epidemics across different scenarios. And we're going to have data for the number of infected animals in different herds, and we're going to make useful comparisons. We're going to do this in a step by step process, which will follow exactly the steps that I would follow as a scientist in order to analyse this data, starting with data filtering and data cleaning, observing the data, calculate some basic statistics, and visualising the data and try to critically examine what we see and discuss and try to interpret the findings.

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