Research Fellow in Mathematical Modelling of Tuberculosis Control

Location: London Campuses

Job Summary

The post is a great opportunity to develop a broad range of skills and experience, inform public health policy and practice, and build an international network of collaborators. TB is a major cause of morbidity and mortality globally, and remains important cause of health inequality in the UK, associated with ethnicity, migration, and social deprivation. We collaborate with the UK Health Security Agency, University College London, and several other universities and hospitals. Our research focuses on understanding the natural history, epidemiology, and transmission patterns of tuberculosis and (cost-)effectiveness of control measures, including to combat drug resistance. We combine infectious disease modelling with studies of patient and population behaviour, health services research, and health economics.

This is an exciting time to be working on TB, with increased political interest in addressing health inequalities, combined with advances in our understanding of TB’s natural history and epidemiology, and development of new diagnostics, treatment regimens, approaches to supporting adherence to treatment, case-finding approaches, and vaccines. Constrained budgets mean that interventions need to be effective and cost-effective, making our work assessing new technology and approaches particularly important.

Our work has informed use of molecular diagnostics for TB (published in Health Technology Assessment, Thorax, and Scientific Reports); our finding that the TB Find & Treat service for homeless persons was cost-effective (BMJ) saved it from closure and led to its expansion; our trial of video observed therapy to support treatment adherence (Lancet) led to its becoming standard practice; our analysis of migrant screening for latent TB infection (Lancet and Lancet Infectious Diseases) led to expansion of the eligible group; our work for NICE informed changes to latent TB screening & treatment guidelines. We also perform studies to improve understanding of the fundamental natural history, e.g. in collaboration with Harvard.

Duties and responsibilities

The post will particularly focus on analysis to inform policy, and developing a tool for decision-makers to assess cost-effectiveness of intervention options at local level. This tool will be based on a national-level individual-based model but will be simplified to make it suitable for use by people who are not expert modellers.

Specifically, you will contribute to:

• Developing an individual-based transmission-dynamic health-economic model of TB in the UK – you will contribute in the way most suited to your skills, such as statistical analysis, synthesising multiple datasets, developing Bayesian model calibration approaches, coding in R, Python, or C/C++.
• Analysing data from observational studies and surveillance to refine estimates of natural history parameters (e.g. risk of progression from latent infection to active TB) and performance (i.e. sensitivity, specificity) of diagnostic tests.
• Analysing surveillance data, and demographic & immigration data, to understand trends in TB incidence by age, social/ethnic group, country of origin, etc.
• Developing a modelling tool for local-level decision-making.

Essential requirements

PhD with relevant data-analysis and coding skills

• Extensive knowledge of infectious disease epidemiology / modelling
• Either (i) strong skills in coding in R, Python, or C/C++, or (ii) strong skills in statistical analysis (ideally in R), and ideally both
• A record of high-quality publications in respected peer-reviewed journals

Further Information

The post is full time fixed term until 31 March 2025, based initially at the St Mary’s Campus, Paddington.

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