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Double funding success for Mike Tildesley

It is great to annouce that Mike Tildesley has been awarded two major grants through BBSRC in the passed two week:

  • Tildesley and Keeling have been awarded a BBSRC grant (in collaboration with Nottingham University) for "Investigating the impact of farmer behaviour and farmer-led control of infectious disease outbreaks in livestock".

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.

  • Tildesley has won a EEID NSF-BBSRC US UK collaborative grant (in collaboration with collegues at Penn State) for "Adaptive surveillance and control for endemic disease elimination".
Quantitative observations of infectious disease incidence define the scope of human and animal health problems, and, over time, reveal the underlying dynamics that allow us to develop, and then evaluate, effective control strategies. As such, surveillance systems are crucial to developing evidence based policy. Developing an efficient surveillance network requires knowledge of the system under surveillance. This proposal develops a framework for the evolution of surveillance systems and their adaptation in tandem with the development of models to support decision-making, changes in control and elimination policies, and the evolving dynamics of disease transmission in response to control. In many settings where surveillance networks remain rudimentary (e.g. in under-resourced settings or diseases that are not yet considered priorities), development of evidence-based policy is hampered by lack of locally-specific demographic, surveillance, serotype, and movement/connectivity data. While efforts to collect these data will always advance scientific understanding, we propose that model-based prioritization of data collection and surveillance system design will lead to more efficient targeting of data collection to specifically support disease control and eradication goals. This work will develop general methods for model-based prioritization of data collection and surveillance system design using highly resolved, long-term, strain-specific surveillance data on foot-and-mouth disease (FMD) virus incidence in Turkey from 2001-2012.
This proposed work will develop the first country-scale, strain-specific models of endemic FMD transmission. FMD is a significant livestock disease in many low and middle income countries (LMICs) and the models developed here will provide insight into the mechanisms of FMD persistence and serve as a guide for the development of locally specific models to support policy development in endemic countries. This proposal will develop novel methods for developing hybrid surveillance systems to efficiently mix 1) passive surveillance with model-based allocation of active surveillance effort, and 2) field diagnosis with diagnostic confirmation.
The data available for Turkey reflect a "gold standard" for what could be achieved in counties that are currently developing FMD control and elimination plans. The methods that will be developed in this proposal will address the iterative development of empirical models and surveillance systems for FMD and will provide a roadmap for countries that currently do not have highly resolved surveillance data. The co-investigators will engage with FMD managers in Turkey, through annual meetings, to transfer models and insights on surveillance and control strategies, and to develop additional control strategies to be evaluated with the models developed herein. The co-investigators will further engage with FMD managers in Kenya and Uganda through existing EuFMD training workshops to develop applications of lessons from the Turkey case-study in those countries. Many control programs for endemic disease suffer from limited, or imperfect, data collection. The examples and methods developed in this proposal will be broadly applicable to human and animal disease settings where surveillance, models, and policy need to be developed in tandem. The co-investigators for this proposal have a long history of engagement with both human and animal health policy organizations and will work to translate lessons from the FMD case study to additional settings. Junior project staff will be trained on the methods developed in this proposal in application to FMD and encouraged to develop applications in other endemic animal and human disease systems (e.g. avian influenza, measles, rubella, meningitis, rotavirus) studied by the assembled co-PIs. These individuals will serve a valuable role in the onward translation of quantitative science to regional and national agencies throughout their careers.
Thu 11 Apr 2019, 11:05