Zoonotic diseases: Modelling Lassa fever in Nigeria

Throughout my PhD, I aim to build upon the foundations established during my MSc dissertation on Epidemiological Modelling of Lassa fever (LF) in Nigeria. I will develop a suite of mathematical models, primarily systems of ordinary differential equations, to robustly describe the spatio-temporal dynamics of LF throughout Nigeria. I will be fitting various models to data using an Abstract Bayesian Computation scheme, specifically fitting the transmission rates and other intangible and difficult to directly measure quantities of infection transmission. These models will be fitted to case reports from 2016 onwards for LF, which have been and will continue to be supplied by the NCDC. With these tools, I will gradually build understanding of the epidemiology of Lassa fever within the country and identify areas in which additional data collection may be beneficial. This will in turn highlight the ecological factors involved in driving the seasonal dynamics of Lassa fever and ultimately help guide policy to control LF in Nigeria.
The context of the research - LF, a haemorrhagic disease endemic in West Africa, is spread primarily through contact with the urine or faeces of the common rodent, Mastomys natalensis with spillover to human populations primarily happening during the months of January through to March. Over 2019 there were more than 1,000 confirmed cases in Nigeria with a case fatality rate close to 20%. Reported cases tend to have a strong seasonal signature providing an interesting problem as the drivers behind the dynamics have not been conclusively determined.
The aims and objectives of the research - The research aims to parameterise and quantify transmission rates and seasonally driven aspects of LF in Nigeria and the ecology of Mastomys natalensis - including breeding rates, contact rates and prevalence of the disease within populations by state - fully described within a robust mathematical framework.
The novelty of the research methodology - I will be fitting various models to data using an Abstract Bayesian Computation scheme. Published papers report fitting LF models to data are either likelihood based or lack detail in their methods for Nigeria. Moreover, I will be trying to illuminate the details of the dynamics within the reservoir of Mastomys natalensis to represent the strongly seasonal case reporting. I believe there is much that can be discerned about the spillover dynamics within such a system and the NCDC has provided data more detailed than that which is available in their situation reports on the NCDC website. The situation reports appear to be the sole source of data for some recent modelling papers, so improvements can certainly be made.
The potential impact, applications, and benefits - Furthering the understanding of disease propagation will enable better control policies. It is possible that insights gained from these studies are applicable to other countries and other diseases, such as other vector-transmitted diseases. In the event of not being able to make an appropriately fitting model to the data, the work will potentially identify gaps in our understanding of LF dynamics and thus spur further research in other fields, such as ecology.
How the research relates to the remit - The research fulfils the criteria for research in Mathematical sciences, particularly under the ESPRC theme of mathematical biology as a mathematical model of LF outbreaks would be appropriately classed as a "tool for the mathematical treatment of biological processes operating at any spatial or temporal scale particularly at the population level".
Research area - mathematical Sciences
External Partner - Nigerian Centre for Disease Control (NCDC)

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.