A short course taught by members of the Centre for the Mathematical Modelling of Infectious Diseases.

Outbreak response requires rapid assessment of patterns of transmission and clinical severity to plan timely interventions and their continued evaluation. This requires a combination of surveillance data, analytical methods and mathematical models. To respond to these technical demands in a real-time, we can draw on a software ecosystem of interoperable R packages and analysis pipelines to achieve these tasks in an efficient and effective manner.

This course will provide a practical introduction to infectious disease outbreak analytics and applied transmission modelling. We will focus on providing a conceptual understanding of the problems the R packages are solving and how to complete common tasks within an analysis pipeline. The course will cover: how to use R packages to efficiently clean, standardise and aggregate outbreak data to produce epidemic curves; how to extract and apply epidemiological parameter distributions to estimate key transmission and severity metrics (e.g. reproduction number and case fatality risk); how to examine the implications of ‘superspreading’ events (i.e. individual-level variation in transmission) in decision-making; and how to generate modelling scenarios of disease spread that account for population structure and social behaviour, and use these models to investigate a range of interventions.

All teaching will be done online with a mix of self-study materials and synchronous sessions. This will involve a mixture of on demand lectures, self-directed preparation tasks and tests, group discussions with leading researchers and tool developers, hands-on practical workshops with experienced practitioners, and tailored advice sessions for your own datasets (e.g. individual line lists or daily or weekly case counts and outcomes). Worked examples will be based on direct experience of real-life outbreak response, including Ebola in West Africa, Zika in the Pacific and Latin America, and the global response to COVID-19.

Who should apply?

This course is well suited to field epidemiologists, public health practitioners, PhD students, health data scientists, or mathematical modellers who have had some exposure to the theory and methods for describing individual-level infection data and investigating the dynamics of epidemics, but want tools to do these tasks more efficiently and effectively in R.

Audience needs that this course is not targeted at:

• An introduction to the basics of R.
• An introduction to the basic theory of infectious disease modelling.
• An explanation of techniques for implementing models and inference methods from scratch in R.
• An in-depth theory of how the statistical methods function within the R packages.

Teaching methods

This online course is taught as a series of weekly self-study material and synchronous contact. Sessions will be taught in the following format:

• Self-study material that includes: a weekly lecture on motivating theory and applied context; a coding demonstration to showcase the end-product; three tutorials to solve using R; and one problem to tackle collaboratively on a discussion forum.
• An online synchronous session of 3 hours per week to recap the learning goals, solve a new challenge in small groups (up to 4 students) with the assistance of tutors, report the outputs, findings, and choices to solve it. Plus, additional live Q&A sessions with active outbreak researchers and tool developers.
• Two individual informal short assessments per week to help refine skills in output interpretation and coding, with feedback provided to the whole class including information collected from forums, group challenges and individual assessments.
• Online drop-in sessions of 2 x 60 minutes per week with tutors, where students can discuss tailoring the application of R tools in their own analysis pipelines.

A Certificate of Attendance will be provided.

The participation in all synchronous sessions is highly encouraged. According to demand and applicant location, we will consider running time zone-specific synchronous practical sessions or further iterations to accommodate different time zones.

Course leaflet 2026.