Modelling for Decision Science will provide you with a technical overview of current modelling methods and equip you with skills that can be used to perform impact and cost-effectiveness evaluation studies in healthcare. This is increasingly relevant for bodies such as the National Institute for Health Research (NIHR). You will be exposed to a range of mathematical and statistical modelling techniques, with a focus on utilising these applied methods to real life problems in public health and healthcare. The course will be taught in lectures and parallel practical sessions using MATLAB or R software, in which you will gain hands-on experience in the analysis and interpretation of results.

There may also be guest lectures from applied statistics and a global health economics consultancy partner.

After taking this module you should be able to:

1. Understand how mathematical and statistical modelling can be applied to public health and healthcare.

2. Apply intermediate modelling techniques, appropriately and specifically for the purpose of evaluating the impact and cost-effectiveness of different interventions. You will have developed the necessary practical skills to design models, perform different analyses and interpret the results.

3. Understand the difference between mathematical and statistical modelling and be able to choose and apply modelling frameworks to address existing and emergent questions from policy decision makers.

4. Understand what machine learning is and be able to differentiate aspects of different order statistics in applied health.

5. Understand different approaches of modelling infectious diseases including simple compartmental models such as SEIR models, more complex agent-based models and rule-based models. You should accumulate skills to understand the assumptions within the models, as well as be able to run and check the models and their outputs.

6. Be able to solve simple mathematical and statistical models using numerical software such as MATLAB or R.

The module is open to anyone with a 2.1 or above in a quantitative first degree.

You will be taught the material through lectures with practical sessions following the lecture material closely and giving you a hands-on approach. Additional problem sheets will be provided to encourage self-study. Moodle will be used to provide a repository of course materials, and in other ways at the discretion of the lecturer.

Reading list:

1. Mathematical Biology: I An introduction by James D Murray

2. Decision Modelling for Health Economic Evaluation (Handbooks for Health Economic Evaluation) by Andrew Briggs

3. Methods for the Economic Evaluation of Health Care Programmes (Oxford Medical Publications) by Michael F. Drummond

4. Machine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning by Peter Bradley

5. An Introduction to Infectious Disease Modelling (Oxford University Press, 2010) by Emilia Vynnycky and Richard White