Spatiotemporal variation of COVID-19 case time series in the United Kingdom: a dynamic time warping analysis

Background: Analysing the spatiotemporal variation of COVID-19 disease activity can help to identify underlying COVID-19 transmission dynamics and inform the targeting of public health responses. We explored the use of Dynamic Time Warping (DTW), a time series analysis method, to investigate the spatiotemporal variation in COVID-19 disease activity in the United Kingdom (UK) from 1 November 2020 to 19 May 2022. Methods: We performed a DTW analysis on COVID-19 case time series stratified by the 380 Lower Tier Local Authorities (LTLAs) in the UK. First, we performed a hierarchical clustering analysis (HCA) of these time series to investigate their similarity. We then inferred the lead-lag time relationship between time series, both over the entire study period and for three distinct periods of variant emergence (Alpha, Delta and Omicron BA.1). Results: Our clustering analysis found that case time series for LTLAs of Wales, Scotland, Northern Ireland and England were closely related to LTLAs within the same nation. We identified groups of LTLAs in England with highly similar trends in case time series; these groups of LTLAs were geographically clustered despite the methodology not using any geographic information as input. The lead-lag time analysis in England showed that LTLAs in southeast England, the Manchester area, and in London each led the Alpha, Delta, and Omicron BA.1 epidemic waves respectively. Conclusion: We quantified geographic heterogeneity of COVID-19 case time series in the UK using a DTW analysis. The results of our lead-lag time analysis concurred with findings of phylogeographic studies. Further studies to determine DTW optimal settings are critical to maximising the potential of DTW in describing the spatiotemporal variation of infectious diseases such as COVID-19.