| The course runs from 7 July to 1 August 2025. |
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, and 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 video 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.
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.
Week 1
- Efficiently clean and standardize individual-level case data (i.e., line lists): standardise different date and number formats, understand patterns of missing data, check sequence of dated events, and perform dictionary-based substitutions from clean hand entered categories like sex or location.
- Protect your real-time analysis against frequent changes and updates in input data like new entries, new variables, and renamed variables, by using validation rules.
- Produce epidemic curves to explore patterns of disease spread by different groups and time intervals using individual-level case data (i.e., line lists).
- Apply best practices for reproducible analysis and reporting during an outbreak.
Week 2
- Access and analyse common epidemiological parameters (e.g., incubation period, serial interval, and epidemiological delays) from open-source literature search databases stored in newly developed R packages.
- Estimate key transmission metrics, such as the reproduction number, from real-time or retrospective surveillance data.
- Produce short-term forecasts that account for reporting delays, noise and uncertainty in outbreak reporting.
- Estimate the case fatality risk (CFR) from individual-level and aggregated incidence case and death data, adjusting for delays between onset of symptoms and disease outcome.
Week 3
- Estimate and compare the dispersion parameter, a critical measure of superspreading events, from contact tracing data.
- Summarise the proportion of transmission that is linked to 'superspreading events', and implications for forwards and backwards contact tracing.
- Generate short-term simulated projections that account for randomness in early outbreak transmission.
Week 4
- Access and analyse reported epidemiologically relevant social contact mixing data from different countries.
- Assess outbreak risk (i.e., the potential for sustained transmission) under different immunisation scenarios, accounting for age patterns of social contact and vaccine coverage.
- Create simulated epidemic scenarios that account for population structure to compare the impact of non-pharmaceutical and pharmaceutical interventions.
- Produce scenario visualisations to communicate key findings to wider audiences
This curriculum features a range of methods and R packages that have been used previously in real-life outbreak preparedness and response, including open-source packages from the Epiverse-TRACE, Epiforecasts, and Reconverse toolkits.
“All excellent! Engagingly delivered and quick to answer questions and point to additional resources.”
“This course should definitely be recurrent and ongoing. The presenter was really great at explaining concepts, providing one-on-one assistance and understanding his material.”
“Learning goals are clear and achievable.”
“The teaching material was very well thought out, plentiful and easy to follow.” “Tutorials are well developed and I appreciate the papers being linked that inform the theory behind the packages.”
Fees
£1000
Fee deadline
26 May 2025
Funding
Special tuition fee rate for offer holders from LMICs
Additional discounts are available for offer holders from Low- or Middle-income countries (LMICs). All LMIC offer holders will receive a 50% discount on the standard course fee of £1000 (i.e. course fee: £500). Up to three LMIC offer holders can also receive a full scholarship (i.e. course fee: £0) if they would otherwise be self-funding their attendance on the course with no other source of support.
Eligibility Criteria for 50% and 100% discounts:
Applicants who wish to apply for these special tuition fee rates
- must hold an offer of admission for the course and
- must be a national of, and currently be resident in, a Low- or Middle- income country (LMIC) - World Bank definition
Additional eligibility criteria for a full scholarship:
- must be self-funding the course fee (i.e. no other source of support available)
How to apply for a 50% discount
Applicants only need to submit proof they meet the eligibility criteria at the time of admission
How to apply for a full scholarship
Applicants need to submit a statement of 250 words to shortcourses@lshtm.ac.uk clearly stating their eligibility for this special fee and a short explanation of how they expect the skills they learn on this course will assist their future work and increase its impact. This explanation will be used for selection if more than three applications for the full scholarship are received.
Please state ‘Outbreak analytics and applied modelling in R’ in the subject line.
The deadline for submission of special fee applications is Monday, 28th April 2025. Decisions will be confirmed within approximately two weeks of the deadline date.
Applying for this course
Applications for 2025 are now open and can be made via our online application form.
Please read LSHTM's Admissions policies prior to submitting your application.
LSHTM may cancel courses two weeks before the first day of the course if numbers prove insufficient. In those circumstances, course fees will be refunded.
Prerequisites
This course assumes some basic R knowledge. This course is for you if:
- You can read data into R and make common types of graphs
- You are familiar with functions from ggplot2
- You have started to link analysis tasks together, for example by using the pipe function %>% in the {tidyverse} or {magrittr} packages and/or |> in newer versions or R.
We expect participants to have some exposure to basic statistical, mathematical and epidemic theory concepts, but not necessarily familiarity with analytics or modelling. This course requires learners to be familiar with:
- Statistics: Familiarity with common probability distributions, such as the Normal, Gamma, Lognormal, and Negative binomial distributions.
- Epidemic theory: Familiarity with common epidemiological parameters, such as the incubation period, generation time, and serial interval, and metrics such as the reproduction number.