Tracking the national and regional COVID-19 epidemic status in the UK using directed Principal Component Analysis

TL;DR

This paper introduces a new metric based on weighted Principal Component Analysis to monitor COVID-19 in the UK, capturing spatial and temporal variations and complementing traditional measures like R.

Contribution

The study proposes using principal scores from PCA as a novel, interpretable indicator for tracking epidemic status across regions and waves, addressing heterogeneity issues.

Findings

Principal scores correlate with R and hospitalisations.

The first principal score effectively indicates pandemic status.

Indicator dominance varies geographically and over time.

Abstract

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 which can capture the different spatial scales. These are the principal scores (PCs) 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 are correlated with the trend in R. Hospitalisations 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.