Investigating the impact of farmer behaviour and farmer-led control of infectious disease outbreaks in livestock

Lead Research Organisation: University of Warwick
Department Name: School of Life Sciences

Abstract

The high density of livestock kept on farms means that they are often at risk of outbreaks of infectious diseases, which can spread rapidly both within and between farms. Examples that have affected the UK in recent years include bovine tuberculosis (bTB) and foot-and-mouth disease (FMD), while bovine viral diarrhoea virus (BVD) poses an emerging risk to the industry for the immediate future. For each of these, the general goal is to mitigate the impact of the disease (often by eradicating the infection from all UK farms) whilst attempting to minimise the total economic impact on the livestock industry. Control can be achieved in two main ways: either with prevention policies dictated by national agencies (for example the imposition of a national ban on movement of all livestock) or through preventive measures taken by the farmer (for example voluntary vaccination or tighter biosecurity). This project will determine the scenarios when farmers will take unilateral action or when national measures are required.
We will develop a range of mathematical models that are able to predict the spread of infectious diseases and capture farmers' responses to the changing risks of infection. Models will range from relatively simple simulations that are designed to provide an understanding of the underlying mechanisms, to specific examples fitted to known diseases including bTB, FMD and BVD. These three diseases cover a range of transmission mechanisms and infection types: from slow endemic diseases like bTB, to rapid epidemics like FMD.
A vitally important aspect for this project is robustly predicting the behaviour of farmers. This will also be refined as the project progresses: starting from the simple assumption that each farmer acts to perfectly minimise their expected costs, to including more realistic heterogeneous dynamics as determined by structured interviews with farmers. Using modern quantitative social-science approaches will allow us to analyse farmers' altruistic behaviour, level of trust and uptake of control. This will be coupled with elicitation to provide us with a set of distributions of behaviour and response to outbreaks that we will incorporate into our models, such that individual farmers will react differently, based upon their perceived risks and benefits as well as their sets of beliefs. This inclusion of farmer behaviour may modify the effectiveness of any nationally imposed control policy, and our predictions will therefore inform policy makers regarding how they should respond to outbreaks. The ultimate outcome will be a robust prediction of how important infectious diseases of livestock can be better controlled to minimise impact on both individual farmers and the livestock industry as a whole. In particular, we will investigate when and how national agencies can ensure active compliance of farmers with disease control regulations.
Given the nature of this grant, communication with livestock policy makers, agricultural agencies and farmers is crucial. We will liaise closely with all relevant agencies throughout the project and provide access to simple graphical user interfaces (GUIs) for our suite of mathematical models that will allow stakeholders to visually assess the risks associated with livestock disease outbreaks and the role of multiple interventions.

Technical Summary

Combating infectious diseases requires the cooperation of all involved parties; whether this is parents vaccinating their child, use of bed-nets to reduce the risk of vector-borne infections or the actions of farmers to protect their livestock. These behaviours can be conceptualized using ideas from game-theory, where each individual is trying to minimize their losses (e.g. escape infection for the minimum effort). We build on such ideas to address key factors controlling infectious diseases within the livestock industry, and the inevitable tension between optimal farmer behaviour and nationally optimal policies. This is achieved by bringing together modern quantitative tools from social sciences and state-of-the-art simulation modelling, based on an in-depth knowledge of livestock disease epidemiology.
We will develop stochastic spatial models of livestock infectious disease spread through the farming landscape, together with the probabilistic reaction of farmers to their locally perceived risk. Our first assumption will be that farmers react perfectly rationally and fully optimize their behaviour. However, this will be refined through formal elicitation to capture heterogeneity and uncertainty in farmer behaviour. We will then build on our existing strengths in modelling foot-and-mouth disease and bovine tuberculosis to incorporate data from the elicitation study to predict the likely impact of realistic responsive farmer behaviour in the control of these diseases. We will also develop new models for bovine viral diarrhoea virus, which is a growing burden to the UK cattle industry and for which a voluntary national control campaign has recently started.
Our overarching hypothesis is that by quantifying the heterogeneity in the reaction of farmers and incorporating this into predictive models, we can provide stakeholders with a better understanding of the need for nationally-led action, the compliance of the farming industry and the probability of eradication.

Planned Impact

Given the applied nature of the research in this project, obtaining impact is a key priority area and will be achieved through a dedicated work-package, which will interface with stakeholders to both shape our research questions and disseminate our findings. A wide variety of individuals and groups within the UK (and overseas) will directly benefit from our findings.
Firstly, a range of policy makers and associated organisations will be consulted during the early stages of our investigation to help shape the control measures that are assessed for each disease. We will communicate regularly with these groups who will also be given access to our findings through policy-ready documentation that outlines our main results in clear non-scientific language. These groups include government agencies (such as the Department for the Environment, Food and Rural Affairs and the Animal and Plant Health Agency) and commercial organisations (such as the National Farmers Union and the Dairy Levy Board) as well as international agencies (such as FAO and EuFMD). These groups will benefit from a greater ability to assess the interaction between policy recommendations, economics and farmer behaviour for a range of prominent livestock infections. In addition, they will be able to access and utilise graphical versions of our code, providing more intuitive insights into the predicted epidemiological impact of different policies. This will help to ensure that future policy decisions or practical recommendations are appropriate for the disease, region under consideration and based on the latest epidemiological forecasts. We will also develop a project website in year 1 that will be regularly updated throughout the grant to provide the most up to date information regarding the progress of our research. The investigators in both Warwick and Nottingham have a long and excellent track record of working with and supplying advice to such organisations that will assist in disseminating this information.
Secondly, those commercially involved with cattle farming (both farmers and veterinarians) in the UK will benefit from a greater understanding of the risks that different activities pose in terms of bringing infection into the farm environment and intervention strategies that may reduce the risk of disease spread. Cattle diseases such as food-and-mouth disease (FMD), bovine viral diarrhoea (BVD) and bovine tuberculosis (bTB) can be financially damaging to the individual farmer and the industry as a whole, and therefore it is vital that our recommendations to government are resonant with the practicalities of the industry. Similarly, industry support for new policies and recommendations is vital for their success. Our aim is that the cattle farming industry will benefit from focused and practical advice from this project that can reduce future disease risk, thereby safeguarding an important industry while ensuring the population at large has continued access to cheap and safe food sources. A large component of this project is focused towards elicitation of behaviour from farmers; however we very much view this as a two-way process, with graphical models used throughout to communicate our findings to farmers and veterinarians that we hope will help to inform these stakeholders regarding the most effective behaviour that they should adopt in the event of future infectious disease outbreaks. Finally, the public will benefit from greater food security, with a livestock industry that is less susceptible to the economic and epidemiological consequences of infection.
Our over-arching aim is to develop a quantitative understanding of the role played by individual farmers in the control of cattle disease outbreaks and to assess when appropriate industry-led intervention strategies can reduce disease burden. This aim, together with our dedication to interact with a range of stakeholders, should provide substantial and sustained benefits to the UK farming industry.

Publications

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Description This grant has been in progress since 2019, but the modelling components of the grant have been interrupted by the COVID-19 pandemic, as the PI, the Warwick co-I and the Warwick PDRA have been working full time as part of the Scientific Pandemic Influenza Modelling Group advising the government. However, the Nottingham team have been moving forward with their components of the project. To date, they have carried out several farmer focus groups to characterise farmer behaviour in the event of infectious diseases on their farms. The team have analysed the data from these focus groups to establish how farmer personality types may influence behaviour and their interactions with other farmers and the wider agricultural sector.

Since the previous submission, we have carried out a series of preliminary interviews with farmers and will shortly be carrying out our elicitation process. We have had one publication accepted, in the Journal of Dairy Science, and have two more papers under review. We have developed a GUI app that will be used to elicit farmer opinions regarding their perspectives around risk of emerging outbreaks and when they may implement vaccination and should be in a position to report on these findings in the next submission in 2023.
Exploitation Route When infectious diseases spread in livestock, models are often used to inform control policies but these models often use the simplifying assumption that farms interact with other farms independently of the particular behavioural characteristics of the farmers. This project has sought to elucidate the role of farmer behaviour upon their response to outbreaks of disease. We believe that the outcomes of this funding will enable the agricultural sector to develop a more bespoke response to infectious diseases such that measures take into account likely behavioural responses of the farming community.
Sectors Agriculture, Food and Drink

 
Title Causes of delayed outbreak responses and their impacts on epidemic spread 
Description Livestock diseases have devastating consequences economically, socially, and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact, however, wait-times for these procedures, i.e., response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
URL http://datadryad.org/stash/dataset/doi:10.25349/D9P315