A UK platform for the control of Bovine Viral Diarrhoea:Application of a novel disease simulation model to guide programme development & policy design

Endemic disease in cattle has a substantial negative impact on welfare of cattle worldwide, reduces farm productivity and profitability and sustainability. Endemic diseases persist within populations unless actively controlled. Control programmes for endemic disease in the UK have tended to focused on relatively few key conditions such as Mastitis, Bovine Viral Diarrhoea (BVD) or Johnes Disease, and implemented by individual farms, vets, or small stakeholder groups. Recently the control of endemic disease within the UK was devolved, and BVD is an example where endemic disease control is handled independently by each of the four devolved nations. However, except for brucellosis, no UK nation has officially eradicated any endemic disease. To achieve a step change in endemic disease control and eradication, national and multi-national, coordinated approaches to disease control are required and the aim of this research is to create a novel solution to guide UK national control programmes for BVD. Our solution is to develop an infectious disease simulation modelling framework applicable to all sectors across the UK cattle industry and across all nations within the UK that is capable of simultaneously modelling both cattle populations within farms, the movements and geographical relationships between farms, the different national BVD control programmes and incorporate the behaviours of different stakeholders (farmers, vets, and programme bodies) and farming systems across the entire network from all four UK nations. Use this collaborative approach will ensure development of a disease model is relevant, practical and addresses the needs of each individual country.
The simulation model will be developed to model the status of each animal, including characteristics of immune status (susceptible, exposed/immune) and infection status. We will incorporate this model with an existing simulation model of a whole cattle farm linked to a holistic environmental life-cycle analysis model, REMEDY. Existing test databases will be used to define UK spatial and temporal patterns of BVD and the key epidemiological and spatial parameters to use within the simulation model to represent BVD. To establish a model of between farm spread we will use machine learning methods to create a new classification system for UK cattle farms, based on the herd demographics, spatial and movement data. This will allow a more detailed reflection of the diversity within the UK's farming population and use the newly defined classification system to simulate the trade if cattle across the networks of UK farms. The within-farm infectious disease model and the network simulation models will be combined with the analysis of the test data to create a UK wide national infectious disease simulation model of BVD. A co-design process with stakeholders will be used define current BVD control programmes and future alternative scenarios of interest and define their goals and behaviours relevant to BVD control. The scenarios defined by the stakeholders will be simulated and multiple aspects of the outcomes evaluated, including epidemiological, economic, and environmental components. The results will be presented to stakeholders and the model evaluated on the model's ability to produce informative and impactful outputs capable of influencing stakeholder behaviour and shape future endemic disease eradication programmes.
This research will impact a range of key stakeholders within UK endemic disease control, including animals, vets, farmers, government, by providing vital information on the performance of different disease control scenarios ant the interactions between each of the devolved nations programmes, thus allowing for informed discussions regarding control programme and policy development. The legacy of this model will not only be a model to support BVD eradication but also, a readily generalisable framework for modelling the control of other endemic disease of cattle.

To achieve our research aims we will create a mechanistic SEIR infectious disease simulation model incorporated within a whole farm model, REMEDY. REMEDY is an object-oriented dynamic mechanistic stochastic model, linked to a holistic environmental life-cycle analysis model and can represent multiple farm types. Within the herd simulation, the infection status of each animal will be modelled (naïve, exposed, persistently / transiently infected, resistant). The contact structure within a herd will be influenced by networks reflecting management. We will use existing BVD test data to define the spatial and temporal patterns of BVD across the UK and to define the epidemiological and spatial parameters to use within the model BVD infection. To establish a model of between farm networks we will use machine learning methods to cluster herds based on the demographic, spatial and movement data to identify a more granular classification systems for UK farms. The newly defined clusters will be used within a mechanistic model of cattle trade networks. The mechanistic within-farm and network simulation models will then be combined to create a UK wide national mechanistic infectious disease simulation model of BVD.
Co-designing with the stakeholders, current BVD control programmes and future alternative scenarios will be identified. Stakeholder goals and behaviours relevant to BVD control will be elicited using semi-structured interviews and survey methods to obtain data to parameterise the simulation model.
The current and alternative scenarios defined by the stakeholders will be simulated and epidemiological, economic, and environmental outcomes evaluated for all the UK and also stratified by nation and herd classification to explore the distribution cost/benefits and risk. Additionally, the impact of the model's ability to produce informative and impactful outputs capable of influencing stakeholder behaviour and the goals of BVD eradication programmes will be assessed.