Essentials of Infectious Disease Modelling and Economic Evaluation
Mathematical modelling is an important tool that can be used to understand the dynamics of infectious diseases. Academics working within Bristol Medical School, including those who tutor on the course, are involved in international research on human and zoonotic infectious diseases to help understand epidemics and predict the future burden of diseases, as well as the impact of different control measures to inform policy.
| Dates | 8 - 9 June 2026 |
|---|---|
| Fee | £500 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Course profile
This course aims to cover the essentials of infectious disease modelling including economic evaluation. The course will provide attendees with the ability to start understanding modelling studies and work with modellers.
Please click on the sections below for more information.
Teaching will be delivered online over 2 full days (between 9 and 5pm). Sessions will include a mixture of live and pre-recorded lectures, live group work sessions and allocated time for guided individual tasks.
By the end of the course participants should be able to:
- understand what infectious disease models are and when they can be used;
- be able to actively collaborate with modellers;
- run a simple infectious disease model using pre-written code and adapt the model;
- interpret basic reproduction numbers;
- understand the principles of vaccination and herd protection;
- critically appraise published infectious disease models; and
- understand how to incorporate economic evaluation into infectious disease models.
The course is intended for epidemiologists, public health specialists, policy makers and healthcare professionals who work in the area of infectious diseases (human and animal health).
Although the computer practicals will be in the programming language R, no knowledge of R is assumed.
This course will cover:
- what models can be used for;
- mechanistic versus statis models and examples of models;
- components of a simple Susceptible-Infected-Recovered model;
- the basic reproduction number;
- model parameters;
- designing a model;
- converting a model sketch into equations;
- simulating a model using the programming language R;
- criteria for disease control;
- principles of disease control through vaccination; and
- using models for economic evaluation.
This course is taught by academics from a variety of backgrounds working within Bristol Medical School who are experts in infectious disease mathematical modelling and health economics.
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Software |
Access to a laptop or desktop computer for the duration of the course (joining by mobile/ tablet would be insufficient). This course requires use of R through Posit Cloud. You will need to set up a free Posit Cloud account, instruction for which can be found on our R Installation Instructions page. |
|---|---|
| Recommendations |
Although the computer practicals will be in the programming language R, no knowledge of R is assumed. |
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Participants are granted access to our virtual learning platform (Blackboard Ultra) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 5 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
100% of attendees recommend this course*.
*Attendee feedback from 2026.
Here is a sample of feedback from the last run of the course:
“I liked the explanation of compartmental models, including all SIR variants and derivation of the differential equations used in the models." - Course feedback, June 2026
“Resources were well prepared - worksheets were easy to follow and understand, there were plenty of helpers to answer questions so that each breakout group wasn't too big. I felt that I had a much better understanding of the topic by the end of the two days (having read the pre-reading ahead of time and felt confused, this became much clearer)." - Course feedback, June 2026
“I found the sessions on critically appraising published infectious disease models especially useful. They strengthened my ability to evaluate model assumptions, methods, and findings in a systematic way. I will apply these skills in my future research to better assess existing evidence and improve the design and interpretation of my own modelling work." - Course feedback, June 2026
“Course was well organised, building up from the foundations with flow diagrams through to the equations and R code. A really good introduction to modelling." - Course feedback, June 2026