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MA4M1 Content

Content: Epidemiology by Example was a new module in 2020/21 which focuses on the application of numerical methods to address real-world problems in infectious diseases. Starting with programming for basic infectious disease models, the module will progress on to implementation of stochastic models, fitting models to real-world data, adaptive management of diseases and health economic analyses for decision making. The module is designed to give an overview of key methods currently used in epidemiology research and will be 100% assessed through coursework.

Programming language: R

Aims: Students taking this module will acquire hands-on experience of manipulating mathematical models, implementing appropriate numerical methods and fitting models to data, all of which are essential components of
modern-day modelling for research or industry. By the end of the module, students will have encountered a range of model types which can describe a broad range of important infection systems such as influenza, malaria, measles and soil transmitted helminths. Students will understand how to perform predictive analyses which could inform policy decision making - such as assessing future control interventions including adaptive strategies and health economic analyses.


By the end of the module the student will be able to:

  • Adapt or create infection models within R and perform simulations
  • Perform fitting to data using both frequentist and Bayesian approaches
  • Implement and explain deterministic and stochastic modelling approaches and their situational appropriateness
  • Demonstrate how modelling predictions can be performed and contrast future interventions including adaptive strategies
  • Utilise basic health economic concepts (disability-adjusted life years, willingness to pay, etc.) and methodology
    communicate modelling outcomes in a clear and informative manner
  • Appraise the suitability of different models and their predictions for real-world decision making
    evaluate the role of assumptions in influencing model outcomes

Outline of the module:

This 10-week programme will be partitioned into five, 2-week topics:

  • Simple infectious disease model dynamics simulation and prediction
  • Deterministic vs stochastic modelling approaches (endemic vs outbreak or elimination)
  • Modelling fitting to data (frequentist and Bayesian methods)
  • Health economics for dynamic models and decision making
  • Adaptive management for improved intervention efficacy

There will be 2 formal lectures per week plus 1 structured, lecturer-lead lab session plus 1 support lab.

Coursework: Assessment will take the format of five worksheets to be submitted in weeks 3, 5, 7, 9 and 11. Weighting is 20% for each sheet. Marks will be returned in weeks 4, 6, 8, 10 and during the Easter break so feedback is received before submitting the next worksheet. Submitted documents will be a mixture of LaTeXed solutions and R code.

Suggested reading:

General: M.J. Keeling and P. Rohani, Modelling Infectious Diseases in Humans and Animals, Princeton University Press, 2007 (ISBN 0691116172)

Health Economics: Briggs, Claxton and Sculpher, Decision Modelling for Health Economic Evaluation (2006) (available in print through Warwick library)

Epidemiology Programming in R: Bjørnstad, Ottar N, Epidemics: models and data using R, Use R!, 2018 (Online at Warwick library)

Topic-specific research articles will be suggested as reading during the courseE