DescriptionThe ongoing Covid-19 crisis has had a deep impact on the whole world. In an attempt to understand, predict and ultimately control the Covid-19 outbreak, many mathematical models have been created that describe the major epidemiological processes thought to underpin Covid-19 transmission. These models can be deterministic or stochastic and can have varying levels of complexity. However, these models all contain several uncertain parameters that need to be explored in order to match the model output to observed data (e.g. on Covid-19 case rates or death rates), which itself involves several large uncertainties. The Bayesian paradigm can be used to address this problem, and hence aid our understanding and control of this important disease. In this project the student will learn how to construct and simulate from simple models of Covid-19 of both deterministic and stochastic form. They will explore the behaviour of such models, develop model improvements, and then apply Bayesian techniques to infer the model's rate parameters, using observed data. Many extension are then possible including examining the model's predictive capabilities and how these might feed into decision support. This project will link to continuing work in the Statistics group in the Dept. of Mathematical Sciences, which is actively engaged with several UK wide modelling efforts designed to aid the UK's response to Covid-19.PrerequisitesStatistical Concepts II
ResourcesA good site that makes you aware of the standard traps modellers fall into is Epidemic Modelling 101: Or why your CoVID-19 exponential fits are wrong but do be aware that we will go much further than the models discussed here.
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email: Ian Vernon