Exploring Covid-19 Spatiotemporal Dynamics: Non-Euclidean Spatially Aware Functional Registration

TL;DR

This paper develops a non-Euclidean spatially aware functional registration method to analyze Covid-19 wave dynamics across UK local authorities, accounting for complex spatial dependencies and phase variations.

Contribution

It introduces a novel registration methodology that incorporates non-Euclidean spatial relationships, specifically driving times, to improve analysis of pandemic wave patterns.

Findings

Driving time better captures spatial dependency than geographical distance.

The adapted registration method outperforms non-spatial alternatives in simulations.

Quantitative analysis of wave characteristics reveals insights into Covid-19 phase variations.

Abstract

When it came to Covid-19, timing was everything. This paper considers the spatiotemporal dynamics of the Covid-19 pandemic via a developed methodology of non-Euclidean spatially aware functional registration. In particular, the daily SARS-CoV-2 incidence in each of 380 local authorities in the UK from March to June 2020 is analysed to understand the phase variation of the waves when considered as curves. This is achieved by adapting a traditional registration method (that of local variation analysis) to account for the clear spatial dependencies in the data. This adapted methodology is shown via simulation studies to perform substantially better for the estimation of the registration functions than the non-spatial alternative. Moreover, it is found that the driving time between locations represents the spatial dependency in the Covid-19 data better than geographical distance. However, since driving time is non-Euclidean, the traditional spatial frameworks break down; to solve this, a methodology inspired by multidimensional scaling is developed to approximate the driving times by a Euclidean distance which enables the established theory to be applied. Finally, the resulting estimates of the registration/warping processes are analysed by taking functionals to understand the qualitatively observable earliness/lateness and sharpness/flatness of the Covid-19 waves quantitatively.