Abstract
Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Highlights
The unprecedented pandemic of the coronavirus disease 2019 (COVID-19) is affecting more than 200 countries and infected more than 32 million causing 578 530 deaths in the USA as of 9 May 2021 [1]
A recent study has discovered the percolation process and phase transition in human mobility networks on the county level, which indicates the possibility of devising effective strategies to control mobility flows at critical bridges and contain the transmission of COVID-19 [31]
COVID-19 is more likely to spread within each subcomponent without transmitting to other ones. Another finding is that prior to αqc, we discover a less significant phase transition at αqc2 where the Metropolitan Statistical Area (MSA) can be partitioned into subcomponents
Summary
The unprecedented pandemic of the coronavirus disease 2019 (COVID-19) is affecting more than 200 countries and infected more than 32 million causing 578 530 deaths in the USA as of 9 May 2021 [1]. A recent study has discovered the percolation process and phase transition in human mobility networks on the county level, which indicates the possibility of devising effective strategies to control mobility flows at critical bridges and contain the transmission of COVID-19 [31]. It is unknown if percolation processes govern the structural changes in mobility networks with a higher geographical granularity or multi-level mobility networks. These strengths make the approach effective in better understanding the hierarchy of dynamic mobility networks and unearth variations in mobility stabilization and emergent structure patterns
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