2026-27 Project (Bottomley & Gallagher)

Improving Interrupted Time Series Analysis for the evaluation of vaccines and other public health interventions

SUPERVISORY TEAM

Supervisor

Professor Christian Bottomley at LSHTM
Faculty of Epidemiology & Population Health, Department of Infectious Disease Epidemiology and International Health
Email: christian.bottomley@lshtm.ac.uk

Co-Supervisor

Dr Kate Gallagher at LSHTM
Faculty of Epidemiology & Population Health, Department of Infectious Disease Epidemiology and International Health
Email: katherine.gallagher@lshtm.ac.uk

PROJECT SUMMARY

Project Summary

Interrupted time series (ITS) analysis is an important tool for assessing public health interventions that works by comparing data before and after the introduction of the intervention. It has played a key role in evaluating the introduction of vaccines such as the pneumococcal and rotavirus vaccines. Many models have been proposed for ITS analysis, but often the models only perform well in very specific situations and do not generalise well to different settings. Furthermore, epidemiologists, who frequently conduct these analyses, often lack training in time series modelling making it important to find robust, user-friendly approaches. This project aims to address this need by developing robust ITS analysis methods that can be implemented by epidemiologists to assess vaccine impact.

Project Key Words

interrupted time series, vaccine effectiveness

MRC LID Themes

Health Data Science
Infectious Disease
Global Health

Skills

MRC Core Skills

Quantitative skills

Skills we expect a student to develop/acquire whilst pursuing this project:

Critical reading and evaluation of scientific and statistical literature
Programming in R (and potentially other languages)
Designing and conducting simulation studies to assess statistical methods
Data management and analysis
Scientific writing and presentation of research findings

Routes

Which route/s are available with this project?

1+4 = Yes
+4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

LSHTM – MSc Epidemiology
LSHTM – MSc Health Data Science
LSHTM – MSc Medical Statistics

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training for research degree study and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Day-to-day work (primary location) for the duration of this research degree project will be at: LSHTM – Bloomsbury, London

Travel requirements for this project: As this research is desk-based, no travel is required. However, there will be opportunity for a research visit to Kenya to work with one of the supervisors

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

Minimum standard institutional eligibility criteria for doctoral study at LSHTM
A bachelor’s or master’s degree in statistics, data science, or a related field with a substantial statistical component, such as epidemiology

Other useful information

Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

Interrupted time series (ITS) analysis assess can be used to provide real-work evidence on the impact of vaccines by comparing data before and after the introduction of the vaccine. For example, the impact of a vaccine on mortality can be assessed by comparing yearly mortality before and after the introduction of the vaccine. A wide range of statistical models have been proposed for ITS analysis, but methods that work well in some contexts often perform poorly in others. Key challenges include accounting for non-linear trends, deciding whether to include covariates, handling outliers, and dealing with autocorrelation and heterogeneity of variance. There is therefore a need for robust, user-friendly methods that can be applied reliably across settings. This project will evaluate different ITS models and develop practical guidance on their use to estimate vaccine effectiveness.

Project objectives

Review existing methods for conducting ITS analysis
Evaluate and adapt existing methods with a view to improving estimates of vaccine impact
Explore the role of covariates in ITS analysis
Develop software and guidance for analysing interrupted time series

Techniques to be used
A wide variety of times series models will be explored including segmented regression, ARIMA models, Bayesian Structural Time Series and user-friendly time series forecasting models such as PROPHET and CausalImpact. The project will evaluate these methods through simulation studies and by applying them to existing datasets. In particular the project will use data from the Kilifi Health and Demographic Surveillance System in Kenya on invasive pneumococcal disease and rotavirus disease, which includes relevant covariates, and a publicly available dataset (Our World in Data) on timing of vaccine introduction and mortality in children under 5 years.

Potential risks to the project and plans for their mitigation
As this is a desk-based project using existing and publicly available data, no major risks are expected.

The student will require skills in time series analysis as well as data management and analysis in R. Depending on their training needs, relevant courses at LSHTM, other London universities, or online will be explored.

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Bottomley & Gallagher Recording

MRC LID LINKS

To apply for a studentship: MRC LID How to Apply
Full list of available projects: MRC LID Projects
For more information about the DTP: MRC LID About Us