Spatial and epidemiological modelling for wildlife and agricultural health

This project will develop spatial and epidemiological models for wildlife and agricultural diseases. It will use two distinct host-parasite systems where there is a need for complex models to address fundamental applied issues. Although the two applied problem areas (trichomonosis in UK finches, and uptake of Integrated Pest Management by UK farmers) involve different stakeholders, different data issues and different epidemiology, they are linked by a commonality of approach and modelling challenges. Key questions applied to both systems include: using model-fitting to understand the disease; assessing the outcomes of disease control policy; and assessing feedback across scales. The first two chapters will concern the use of mathematical modelling in understanding the finch trichomonosis outbreak, which has been ongoing since 2005, and has resulted in drastic declines in British greenfinch and chaffinch populations. The availability of population and disease incidence data allows for
the development and fitting of detailed mathematical models for this disease system. The core research questions which we seek to address are: (1) can we infer the spatial patterns in the disease dynamics from routinely collected wildlife data sets, (2) what mechanisms are driving seasonality. Initially, a deterministic time-dependent disease model will be developed. A key aspect of the model development methodology will be working with the BTO and IoZ experts in constructing a functional representation of the relevant biological parameters, such as contact rates, density dependence of disease, and population birth and survival rates. This model will be fitted to the population and disease reporting data using likelihood optimisation methods (maximum likelihood and MCMC), allowing us to infer mechanisms and routes of infection. This model and corresponding fitting will then be extended to include the spatial dimension of the data. The next chapters will focus on agricultural diseases. This will involve the integration of farmer-behaviour models with crop disease models, in order to investigate strategies to reduce dependency on chemical pesticides and increase the uptake of Integrated Pest Management (IPM) by farmers. Currently the use of pesticides continues to rise; in large part due to the perception by farmers that the alternative disease management strategies, such as Integrated Pest
Management (IPM), are difficult and costly. In order to encourage the uptake of IPM by farmers, the UK government offers a scheme of incentives, primarily as payments per-year. Behavioural modelling
can be used to guide incentive schemes by evaluating the impact of various IPM adoption outcomes on the disease system. Two key questions which this project will aim to answer are (1) how successful would varying levels of uptake of IPM be at controlling disease, and (2) what fraction of farmers would need to initially take up IPM in order for it to be successful at controlling disease. This problem can be approached using a deterministic ODE model initially; investigating the outcomes from different initial conditions, and different parameter values dictating the interactions between the disease system and the farmer-behaviour system. Later methodological approaches can then be expanded to include stochastic frameworks, which explore localised or individual-based dynamics.
The intention is to begin this work by looking at yellow rust in cereal crops. Models already exist for this disease system, and there are a number of well defined control strategies which are known to work. One such strategy involves the use of resistant crop varieties, which are important for yellow rust control, but which farmers find challenging because of the need to change varieties frequently since resistance can be quickly overcome.

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.