Introductory description

This module is a practical course in learning and applying techniques in mathematical modelling of infectious disease dynamics. There is a strong emphasis on using programming and problem solving to address a series of key questions arising in the field of mathematical epidemiology.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

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

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

Each fortnight students will learn about current real-world motivating problems and methodologies available to address them. There will be 2 lectures a week + 1 computer lab session with the lecturer + 1 TA-guided lab session. During these 10 weeks students will cover a range of different infections spanning contagion, vector-borne disease, and macroparasitic infections. Diseases of humans and animals will be covered.

Learning outcomes

By the end of the module, students should be able to:

• (a) adapt or create infection models within Matlab, and perform simulations
• By the end of the course the student will be able to:
• (b) perform fitting to data using both frequentist and Bayesian approaches
• (c) implement and explain deterministic and stochastic modelling approaches, and their situational appropriateness
• (d) demonstrate how modelling predictions can be performed and contrast future interventions including adaptive strategies
• (e) utilise basic health economic concepts (disability-adjusted life years, willingness to pay, etc.) and methodology
• (f) communicate modelling outcomes in a clear and informative manner
• (g) appraise the suitability of different models and their predictions for real-world decision making
• (h) evaluate the role of assumptions in influencing model outcomes

Indicative reading list

General reading:

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

Health economics:

• Barton, G.R., Briggs, A.H. and Fenwick, E.A., 2008. Optimal cost‐effectiveness decisions: the role of the cost‐effectiveness acceptability curve (CEAC), the cost‐effectiveness acceptability frontier (CEAF), and the expected value of perfection information (EVPI). Value in Health, 11(5), pp.886-897.

Vector-borne infections:

• Rock, K.S., Torr, S.J., Lumbala, C. and Keeling, M.J., 2017. Predicting the impact of intervention strategies for sleeping sickness in two high-endemicity health zones of the Democratic Republic of Congo. PLoS neglected tropical diseases, 11(1), p.e0005162.

Soil transmitted helminths:

• Turner, H.C., Truscott, J.E., Fleming, F.M., Hollingsworth, T.D., Brooker, S.J. and Anderson, R.M., 2016. Cost-effectiveness of scaling up mass drug administration for the control of soil-transmitted helminths: a comparison of cost function and constant costs analyses. The Lancet infectious diseases, 16(7), pp.838-846.

Adaptive management:

• Shea, K., Tildesley, M.J., Runge, M.C., Fonnesbeck, C.J. and Ferrari, M.J., 2014. Adaptive management and the value of information: learning via intervention in epidemiology. PLoS biology, 12(10), p.e1001970.

Research element

Students will be encouraged to engage with recent scientific articles connected to the topics they are studying to help expand their knowledge and see examples of modelling methodologies in practice.

Interdisciplinary

This module falls at the interface of Mathematics, Statistics, Biology and Health Economics. Students will utilise their quantitative background in Mathematics to investigate how modelling can shape our understanding of infectious diseases and guide their control.

International

This module encourages the students to consider infections from the global perspective: from controlling diseases with pandemic potential, to infections endemic to different geographies - especially ones present in tropical and/or developing countries. Through the health economic concepts, students will explore ideas surrounding the value of health and gain an international perspective on how health policy decisions are made.

Subject specific skills

• Implementation of ODE and stochastic dynamical systems in Matlab
• Improved understanding of how to develop models in epidemiology
• Understanding of model fitting (MLE and MCMC) methods (statistics)
• Ability to utilise key health economic concepts in epidemiological dynamic modelling
• Comprehension of practical optimisation methods for real-world decision making

Transferable skills

• Problem solving including creative solutions
• General programming skills
• General modelling skills
• Research skills (reading scientific articles and implementing/modifying models)
• Using LaTeX