Modelling vector-borne disease epidemic risks using forward climate projections

A large amount of work has been done in the field of vector-borne diseases. Work from here can be used to build models for vector-borne diseases of interest (such as Zika virus or Dengue fever). This is a very frequently researched area
and so models of these diseases are robust and have been shown to make sensible real-world predictions. Studies have also been done on climate dependent mosquito population models [3,4]. These models vary in their complexity and include parameters such as temperature, humidity and rainfall to provide forward projections for the population of mosquitoes. Such models can be used in this project and embedded within the aforementioned epidemiological models to create systems where climate varying epidemic risks can be studied. These models have been used to estimate the reproduction number of a disease and to simulate outbreak dynamics but not to estimate the risk that early cases generate an epidemic. The IER is frequently examined because it is a very standard result in epidemiology and is widely used in many studies [5], particularly those in which climate effects on epidemic risks are not accounted for. The IER is often
computed using a population model embedded inside an epidemiological model as detailed above. However, there is very little work done on the CER. The CER has been examined in a basic host-vector model but this only allows for varying
death rates (and not varying population of mosquitoes) [6]. Studies on the CER do not use climate change forward projections, and instead focus on local change in climatic conditions over the course of a year. This research fits well into the
broader context of the field because very little work has been done on the CER with none having been done using real-world climate projections.
This project aims to answer several questions in the field of epidemic risk and vector-borne diseases. Are vector-borne diseases more likely under a changing climate? Is it the case that climate change uniformly increases the risk of large
outbreaks of vector-borne diseases occurring or are there geographical areas where the risk is likely to decrease? What is the distribution of regions that are high and low risk? Other questions to be answered include, is the IER a suitable approximation for the epidemic risk or does the CER need to be computed? Can these two theoretical quantities be related to practically useful outbreak risk metrics, such as the probability of an outbreak exceeding a certain number of cases? How much variation is there in epidemic risk in a given region when the initial conditions for the climate simulations are varied? Do there exist places where the epidemic risk is consistently high or consistently low?
This research closely relates to the EPSRC in both content and wider goals. The content is in the field of mathematical epidemiology where novel methodologies will be used to construct a mathematical framework for predicting the outbreak
risk of vector-borne diseases. This project also extends to the physical sciences (due to climate change data), living with environmental change and global uncertainties. Particular emphasis will be placed on communicating research outcomes with both the external partner and the scientific community as a whole. There is also potential for public outreach and generating awareness of the topic.

Research areas; Global uncertainties, LWEC [Living With Environmental Change], Mathematical Sciences, Physical Sciences
External Partner; Colorado State University, and National Center for Atmospheric Research, USA

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.