Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis

Ben Swallow*, Wen Xiang, Jasmina Panovska-Griffiths

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)


One of the difficulties in monitoring an ongoing pandemic is deciding on the metric that best describes its status when multiple intercorrelated measurements are available. Having a single measure, such as the effective reproduction number R, has been a simple and useful metric for tracking the epidemic and for imposing policy interventions to curb the increase when R > 1. While R is easy to interpret in a fully susceptible population, it is more difficult to interpret for a population with heterogeneous prior immunity, e.g. from vaccination and prior infection. We propose an additional metric for tracking the UK epidemic that can capture the different spatial scales. These are the principal scores from a weighted principal component analysis. In this paper, we have used the methodology across the four UK nations and across the first two epidemic waves (January 2020-March 2021) to show that first principal score across nations and epidemic waves is a representative indicator of the state of the pandemic and is correlated with the trend in R. Hospitalizations are shown to be consistently representative; however, the precise dominant indicator, i.e. the principal loading(s) of the analysis, can vary geographically and across epidemic waves.

Original languageEnglish
Article number20210302
Number of pages15
JournalPhilosophical Transactions of the Royal Society. A, Mathematical, Physical and Engineering Sciences
Issue number2233
Early online date15 Aug 2022
Publication statusPublished - 3 Oct 2022


  • COVID-19
  • Multivariate statistics
  • Dimension reduction
  • Spatial epidemiology
  • Principal Component Analysis


Dive into the research topics of 'Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis'. Together they form a unique fingerprint.

Cite this