Objectives matter: A mathematical modelling framework to identify optimal control strategies for future infectious disease outbreaks

When deciding upon the optimal control intervention during an epidemic, a policy maker might seek to select the strategy that minimises a defined epidemic metric such as cost, duration or the number of severe disease cases. It is most likely, however, that the policy maker in question is required to consider multiple competing objectives which complicates the identification of the optimal strategy. The decision is further complicated by uncertainty, especially if the pathogen is novel and disease-specific parameters or the efficacy of interventions are unknown. The policy maker could therefore benefit from a framework which can recommend control strategies for future infectious disease outbreaks, subject to a cost function which includes multiple objectives. This project aims to develop a mathematical framework which uses a suitable objective function to explore optimisation of control interventions for infectious disease outbreaks. The project will use the framework to investigate how optimal intervention policies are dependent upon the spatiotemporal state of the outbreak, the characteristics of the disease and, crucially, the objective of policy makers when implementing such policies. This framework will be initially applied to models of respiratory virus outbreaks in humans of global significance, such as seasonal influenza. Time permitting, the framework will be extended to consider animal or plant diseases which may require an alternate modelling approach and a different class of control interventions. The project will rely upon the development and simulation of various models of
infectious disease outbreaks whose projections will be used to measure the objective function against. The models must be developed to allow for flexible implementation of feasible control interventions and will be fitted to data (either provided by the external partner or publicly available through previous publications and/or online public health dashboards) using statistical inference, or incorporate plausible uncertainty in disease parameters such as the transmission rate to investigate changes in the objective function. We will develop a suite of models that can be used in a range of different future
infectious disease outbreaks, including considering stochastic models and spatially explicit models. In the case of respiratory diseases, considering a spatially explicit model adds a layer of realism to the modelling as restrictions during the recent SARS-CoV-2 (COVID-19) pandemic were often localised. This can assist the policy maker in identifying vulnerable administrative divisions and taking targeted action. In the context of plant and animal diseases, the spatial heterogeneities are essential for model accuracy as the population can no longer be considered to be well-mixed.

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.