We study the epidemiology of tuberculosis in cattle using a combination of fieldwork and the analysis of big data.

cattle outside woodland


Bovine tuberculosis (bTB) is caused by infection with Mycobacterium bovis. It is the most pressing animal health problem in Great Britain. Around 40,000 cattle test bTB-positive each year and are slaughtered in an effort to control this disease. This comes at a cost to the taxpayer of around £100 million per year in surveillance testing and compensation.

The Government has a strategy to eradicate bTB in England by 2038. If this is to be achieved, we urgently need to improve our understanding of the risks associated with bTB in cattle, both at the individual and the herd level, so that the current bTB control policy can be enhanced. Knowledge of risk factors is not only essential to improve the understanding of bTB epidemiology. It can also help identify which farms would benefit from specific targeted intervention measures to aid disease prevention and control.

cattle inside shed


We conduct research on bTB epidemiology in cattle. Our projects focus on risk, diagnosis, surveillance, and control of this disease. We use a combination of approaches including:

Applying machine learning predictive classification algorithms to big datasets of cattle bTB test results. This enables risk factors to be robustly identified and cattle herds to be classified according to risk status. Knowing which herds are at high risk of spreading infection to others enables them to be targeted for specific additional disease control actions, supplementing the more generalised population-level measures applied to all herds.
Conducting fieldwork using technologies such as proximity loggers (on collars and fixed base stations) to record how often (and when and where) cattle and badgers come into contact. We use this information to inform farm biosecurity measures.
Modelling individual and herd-level changes to cattle bTB testing (including methods of diagnosis) to determine the benefits and costs of potential alterations to the system. This will affect how surveillance for bTB is conducted.

tuberculosis bacterium


Our findings are widely published and regularly shared with Defra. Our research informs the UK Government’s bTB surveillance and management strategies, and well as research studies which aim to better understand the epidemiology of the infection in cattle. Examples of the impact of our research include:

                    • Using our models to identify the characteristics of farms at highest risk of becoming infected. This aids the detection of infected cattle, which is one of the top three current priorities to enhance the bTB eradication strategy.
                    • Simplified easy-to-adopt models were used to inform the targeted application of additional measures in bTB surveillance as well as disease control settings. This is particularly useful in high-risk areas, where their blanket application could be unfeasible or counterproductive (for example, if increasing the sensitivity of detection comes at the expense of an excessive number of false positives).
                    • We put forward proposals that describe how the models we developed could be adopted and deployed, minimizing the impact of their implementation on staff resources. This expands the use of software-based tools to not only gather and present “real-time” information on farm characteristics, but to use that information to run the models, identifying high-risk farms to be targeted with the additional disease control measures proposed.


We collaborate with colleagues at the Animal and Plant Health Agency. Some of our research is funded by Department for the Environment, Food & Rural Affairs (Defra).


Title Publication Year

Zoonotic Tuberculosis – The Changing Landscape

International Journal of Infectious Diseases 2021
A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England Preventive Veterinary Medicine 2021

Mapping the geography of disease: A comparison of epidemiologists' and field-level experts' disease maps

Applied Geography 2021
An assessment of risk compensation and spillover behavioural adaptions associated with the use of vaccines in animal disease management Vaccine 2020

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Preventive Veterinary Medicine 2020

Risk factor analysis of “Diagnosis Not Reached” results from bovine samples submitted to British veterinary laboratories in 2013 to 2017

Preventive Veterinary Medicine


Assessing effects from four years of industry-led badger culling in England on the incidence of bovine tuberculosis in cattle, 2013–2017

Scientific Reports


Exploring the risk posed by animals with an inconclusive reaction to the bovine tuberculosis skin test in England and Wales

Veterinary Sciences


Quantifying and addressing bias associated with imperfect observation processes in epidemiological studies Frontiers in Veterinary Science 2019
Exploring the fate of cattle herds with inconclusive reactors to the tuberculin skin test Frontiers in Veterinary Science 2018
Quantifying direct and indirect contacts for the potential transmission of infection between species using a multilayer contact network Behaviour 2018

Bovine tuberculosis: how likely is a skin test reactor to be uninfected?

Veterinary Record


Risk factors for visible lesions or positive laboratory tests in bovine tuberculosis reactor cattle in Northern Ireland

Preventive Veterinary Medicine


Epidemiology of Mycobacterium bovis

Zoonotic Tuberculosis (3rd ed)


Molecular epidemiology of Mycobacterium bovis

Zoonotic Tuberculosis (3rd ed) 2014

Patterns of direct and indirect contact among cattle and badgers naturally infected with tuberculosis

Epidemiology and Infection 2013

Performance of proximity loggers in recording intra- and interspecies interactions: a laboratory and field-based validation study

PLoS ONE 2012

Top of page