On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions. Author summary Large-scale intensive interventions in response to the COVID-19 pandemic have been implemented globally, significantly affecting human movement patterns. Mobility data provide an empirical measurement of UK travel within a spatially-explicit network, but it is not clear how the structure of that network changed in response to national or locally-targeted interventions. We use daily mobility data aggregated from Facebook users to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. We identified changes in human behaviour in response to interventions and identified the community structure inherent in these networks. This approach can help quantify the extent of strongly connected communities of interaction and their relationship to the extent of spatially-explicit interventions. We show that spatial mobility data available in near real-time can give information on connectivity that can be used to understand the impact of geographically-targeted interventions and in the future, to inform these intervention strategies.
【초록키워드】 COVID-19, Structure, Evolution, lockdown, Human behaviour, COVID-19 pandemic, risk, Intervention, Local, Transmission, Epidemic, Mobility, Travel, Community, understanding, distribution, information, quantification, Intensive, Critical, Interaction, Facebook, connection, These data, Author, help, reorganization, National, resurgence, connectivity, mitigate, approach, decrease, identify, affected, significantly, can be used, were used, determine, changes in, changed, affecting, inherent, 【제목키워드】 COVID-19, Intervention, behavioural, changes in,