DescriptionThere are many physical systems that scientists aim to understand, including the outbreak and spread of COVID-19 through a population. A crucial aspect of understanding such systems is construction of a computer model, which in this case seek to describe the major epidemiological processes thought to underpin COVID-19 transmission. Such models typically represent the system as execution of computer code, for example, numerically solving sets of differential equations. These equations are usually determined by sets of rate parameters, for example, representing the rates of infection of the virus. This project aims to investigate simulation of COVID-19 models for the purpose of making inferences about their rate parameters, and hence corresponding properties of COVID-19 transmission, by matching model output to data. In so doing, we shall discover that many of these models are slow and take time to evaluate. We will therefore utilise Gaussian process regression, or emulation, to statistically approximate computer model output at any finite collection of input points by a multivariate normal distribution. The possible directions for this project are diverse, including analysis of various interventions (e.g. lockdown), particularly as a response to possible virus mutations and vaccination efficacy. Substantial coding (for example, in R) will be necessary to practically carry out and investigate both COVID-19 models and Gaussian process regression.
Prerequisites
Resources
|
email: Samuel Jackson