Estimation and Implication of Time-Varying Reproduction Numbers During COVID-19 Pandemic in the UK

Abstract

The prevention and control of infectious diseases is an important aspect of public health management. Monitoring, modelling and full understanding of epidemic and further providing early warning and policy guidance is crucial to the effective prevention and control of infectious diseases. Firstly, this thesis starts from a review of epidemic models, in particular, for Susceptible-Exposed Infectious-Recovered-Susceptible (SEIRS) model as a typical example of the deterministic (mathematical) approach and for the Estimating Time Varying instantaneous Reproduction ETVR model which is based on a statistical approach. The estimation of the reproductive number R as a valuable index to understand the dynamics of infectious diseases is reviewed as part of the ETVR model. Secondly, the impact on the estimation of R of several modelling parameters in the UK COVID-19 data sets is then evaluated, which provides insight into how to select a set of suitable parameters in modelling. Thirdly, an extended model which incorporates variable reporting rates is developed and applied actual UK COVID-19 data sets for the first time and new insights have been gained. It is found that the reporting rate does not dramatically affect estimates of R as long as the proportion of asymptomatic cases and the reporting rate are constant through time. But when the reporting rate is variable, it will impact on the estimation of R. When the report rate has a step transitions from low to high, it results in an overestimation of R; otherwise, R will be under-estimated if the report rate decreases. If the report rate changes in a continuous fashion, then its effect is more moderate than the step change. These findings provide a caution on drawing conclusions for R when the reporting rate is not constant. Finally, the effectiveness of the interventions in the UK during COVID-19 is critically analyzed and evaluated through studying the trend of the R number based on actual UK COVID-19 data sets in response to intervention measures which has not been reported as detailed in the thesis . Overall, the thesis has studied the epidemic data of COVID-19 and contributed to the epidemiological modelling through qualitative and quantitative analyses including estimating the controlling factors of R and simulating its correspondent transmission scenarios.

Date of Award	31 Dec 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Wuliang Yin (Supervisor) & Frank Podd (Supervisor)