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A longitudinal model for the spread of bovine tuberculosis -Joint funded project with BBSRC - SE3269

Bovine tuberculosis (bTB) is an important disease of cattle and badgers with substantial socio-economic impact in the UK, currently costing the exchequer over £100 million per year in surveillance and compensation and also resulting in costly movement and trade restrictions for farmers. Despite intensive controls, disease incidence is still increasing. Currently herds are monitored for the disease through slaughterhouse surveillance and through regular skin testing. The frequency of routine testing for an individual herd is based on localised incidence of the disease, which acts as a proxy for risk of infection, but does not account for individual herd-level characteristics or cattle movements. Recent bTB research has focussed on examining potential underlying causes for this, including environmental contamination (e.g. re-infection from local wildlife reservoirs), insensitivity to the surveillance test and the impact of large-scale cattle movements. It is the purpose of this proposal to extend our recent work identifying markers for the persistence of infection in individual herds into a dynamic longitudinal framework in order to quantify the mechanisms of transmission in the GB national herd and to test the utility of our results as an aid to risk-based surveillance.

The dynamics of transmission of bTB infection can be represented by a model with transmission driven by chance processes, with an observation process that is governed by an imperfect test procedure (or slaughterhouse identification of visible lesions), leading to partially hidden infection. Herds that contain one or more reactors are classified as breakdowns, which then have movement restrictions and more rigorous testing imposed until the herd tests clear. Testing and cattle movement information is available through several large national datasets. Recent mathematical modelling approaches have been developed using these data and, while these will provide useful information on population-level parameters, they average out some detailed information available at the individual herd level. Also, they were not designed to predict disease recurrence at the individual-herd level. Here we propose to build a dynamic, statistical, individual-herd level model, based on continuous surveillance data, which we will fit to the data using a likelihood-based approach.

The main methodological challenge will be to deal with the hidden states (infection) and the movement of animals between the herds. Recent advances in statistical methodology, such as "data-augmented" and "reversible-jump" Markov chain Monte Carlo allow the joint distribution of the observed and hidden states to be estimated simultaneously along with key infection related parameters. We will explore an exciting alternative called "sequential filtering". The main challenge is that these statistical techniques are computationally intensive, especially given the large scale (approx. 130,000 premises) and long time frame (6+ years) of the datasets. However, advances in computer processing technology, such as architectures for running algorithms in parallel on graphics cards, provide an exciting and cost-effective way to approach this problem. The focus here is on bTB, but these sorts of models and the estimation issues that we will address are relevant to a wide range of infectious disease systems, and the methodology developed in this project would be applicable to a range of disease systems.

It is the aim of this project to elicit information about the hidden states of the system from the test observations using robust statistical methodology, in a way that allows us to identify high-risk herds based on the past history of infection, as well as on localised incidence and connectedness to other premises. This information would have a practical use in terms of targeting specific herds with more stringent or more regular testing.
The main aim of this project is to develop a longitudinal statistical model to explore the mechanisms of spread of bovine tuberculosis (bTB) in the GB national herd and thus to identify improved control policies. The model will take full advantage of the rich level of longitudinal data available, including the Cattle Tracing System, which includes information on animal movements throughout the whole of the GB cattle network. Specific objectives are:
- To develop likelihood-based transmission models for monitoring hidden bTB infections in individual herds over time that incorporate explicit information about the timing and results of both the surveillance tests (SICCT/gamma-IFN/slaughterhouse surveillance) and cattle movements, in order to understand better the mechanisms of persistence and transmission of the disease in the GB national herd.
- To extend these models to incorporate explicit localised spatio-temporal structure by using random effect terms to account for heterogeneity in transmission due to localised mechanisms and persistence of the pathogen (e.g. wildlife/environmental reservoirs/fomites spread).
- To develop fitting mechanisms for these models that can be made computationally tractable as well as cost-effective. This will most likely involves parallel processing, either by using multi-core CPUs, or alternatively through the use graphics processing unit (GPU) technologies.
- To explore whether the model could be used for surveillance, at either the individual herd or parish level. This would help to identify individual herds or regions that may be at a high-risk of harbouring undisclosed infection in order to better inform the targeting of control measures.
- To explore ways in which the parameter estimates and model predictions could be updated as new data becomes available, without having to refit the entire system.
- To incorporate all of the above into a user-friendly interface such that the model could be used for ongoing surveillance by Animal Health, the Veterinary Laboratories Agency or Defra. This would be done using open-source software such as R (possibly using a web-based front-end).
- To explore whether the methodologies could be extended into epidemic situations for real-time model fitting/prediction.
Time-Scale and Cost
From: 2011

To: 2014

Cost: £50,433
Contractor / Funded Organisations
Animal Diseases              
Animal Health              
Plants and Animals              
Fields of Study
Animal Health