2830: Eliminating in country TB transmission in the UK defining the added value of whole genome sequencing
Source: gov.uk (2025-08-04)

This project aims to learn from past cases of tuberculosis in the UK and how they can predict which persons are likely to be infected in the future.

About the project

What the project aims to do

Tuberculosis is a bacterial infection which can be lethal if left untreated. The main aim of this project consists on learning from past cases of tuberculosis in the UK to predict which persons will likely be infected in the future. These results will allow more precise public health interventions that should lead to the elimination of transmission person-to-person of this pathogen in the UK. To achieve this goal, we will:
- analyse tuberculosis genomes (that is, genetic information contained in the bacteria) collected by the UKHSA since 2018 to infer past transmission events
- connect these transmission events to patients data to identify the features most likely associated with tuberculosis transmission
Why this project is important

The number of tuberculosis infections in the UK has been declining in recent years thanks to public health interventions. These measures notably include an automatic tuberculosis detection programme for all migrants arriving in the UK, which account for about 75% of tuberculosis cases (Tuberculosis in England, UKHSA 2021 report).

However, the elimination of tuberculosis community transmissions, causing 25% of tuberculosis infections in the UK, is a costly process involving the tracing and testing of all contacts of the persons infected, and has so far failed in several part of the country. A better understanding of tuberculosis community transmission in the UK is therefore warranted to improve public health interventions and to eliminate these tuberculosis community transmissions.

Who the data is about

The data includes:
- all patient notifications with Mycobacterium tuberculosis (MTB) whether microbiologically or clinically diagnosed, notified between 1 January 2018 and 31 December 2023
- residency in England at the time of sample collection or notification
- mycobacterial sequencing data
- gender (male or female)
- ethnicity
How the data will be used

The tuberculosis (TB) whole genome sequencing data and depersonalised patient’s medical and social data, all already collected by the UKHSA, will be transferred to Imperial College London. TB sequencing data will be analysed to identify transmission events between TB genomes. Using the patient medical (for example, type of TB infection and TB symptoms) and social data (for example, approximate geographical location), we will then identify which TB patients should ultimately be targeted for contact tracing. We will also estimate the cost effectiveness of the proposed contact tracing measures using modelling analyses.

How often data is needed

Data will be released annually.

How this project will benefit public health and the public

We hope this research project will help UKHSA to eliminate TB community transmission by providing a set of rules to prioritise contact tracing in a cost-effective way. For each future TB infection occurring in the UK, our project will predict which person should be prioritised for contact tracing, given the epidemiological and social characteristics of the person infected.

Planned project outputs and communication

The project will be featured in:
- peer reviewed scientific journals
- an internal report (publication not intended)
- a conference presentation
Lawful processing of personal and special category personal data

The data needed for this project is not personal data.

Legal basis for using personal data (Article 6):

Not applicable

Legal basis for using special category personal data (Article 9):

Not applicable

Common law duty of confidentiality

The data needed for this project is not confidential patient information.

How is the duty of confidentiality set aside:

Not applicable.

National Data Opt-Out

Will opt-out preferences be applied? No

Where “No”, why: The National Data Opt-Out does not apply to anonymised data.

Digital Object Identifier:

Not assigned to this release.

Organisatons Research Organisation Registry (ROR) ID:

ROR ID (https://ror.org/041kmwe10)