Revealing spatiotemporal variations in areas potentially linked to COVID-19 spread using fine-grained population data

Abstract

Abstract The COVID-19 pandemic has highlighted the need to better understand the dynamics of disease spread in cities in order to develop efficient and effective epidemiological strategies. In this study, we utilise fine-grained spatiotemporal population data obtained from mobile devices to identify areas and time of day that may contribute to COVID-19 spread, and investigate how they change throughout different waves of the pandemic. To evaluate the potential risk to city residents, we analyse the correlation between the effective reproduction number and population dynamics at locations regularly visited by these residents. Our case study of Tokyo identifies highly-correlated areas at a fine-grained level, revealing shifts in these areas within cities and across urban and suburban regions as the pandemic progresses. We also explore the characteristics of the potential areas of concern through the lenses of points of interest and population dynamics. Our findings have implications for comprehensively understanding the spatiotemporal dynamics of COVID-19 and offer insights into public health interventions for managing pandemics.