Multivariate spatio-temporal analysis of the global COVID-19 pandemic

Abstract

The Covid-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging globa trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global Covid-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality daynamics from early-August, consistent in timing with the emergence of new strains with highger levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.

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

“…In the study by Xiang & Swallow [ 5 ], the authors analysed data from global trends relating to reported cases of, and deaths resulting from, SARS-CoV-2. Their analyses found that a single temporal trend dominated the global spread of the disease.…”

“…a univariate x. As in the study by Xiang & Swallow [5], this aims to remove trends specific to each stream, with residual variation used to determine the PCs. Remaining correlation in the model residuals between time [1, .…”

“…a univariate x. As in the study by Xiang & Swallow [5], this aims to remove trends specific to each stream, with residual variation used to determine the PCs. Remaining correlation in the model residuals between time false[1,…,false(n−1false)false] and false[2,…,nfalse] is calculated for each stream and then the median value is used as an estimate of ρ, the average global temporal correlation.…”

“…We aim to do this by developing an approach similar to Xiang & Swallow [ 5 ] in analysing data from the UK COVID-19 dashboard [ 6 ] to study simpler representations of the multivariate output of cases, deaths, hospitalizations and MVB occupancy. We use S-Mode and T-mode PCA, with a temporal weight matrix calculated from median correlation in residuals following a generalized additive model fitted to a smooth of date index.…”

Challenges for modelling interventions for future pandemics

“…Between-country comparisons often receive significant backlash from politicians and the media and can easily be open to criticism for not accounting for some underlying process that has not been considered (demographic or environmental differences, for example) (Pearce et al, 2020;Xiang and Swallow, 2021;Komarova et al, 2020). Data collection procedures also vary drastically between nations and privacy constraints make large-scale analyses challenging to complete.…”