Zoonotic diseases: Modelling Lassa fever in Nigeria

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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 SciencesExternal Partner - Nigerian Centre for Disease Control (NCDC)