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Percolation of temporal hierarchical mobility networks during COVID-19.
He, Haoyu; Deng, Hengfang; Wang, Qi; Gao, Jianxi.
  • He H; Department of Computer Science and Center for Network Science and Technology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
  • Deng H; Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA.
  • Wang Q; Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA.
  • Gao J; Department of Computer Science and Center for Network Science and Technology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Philos Trans A Math Phys Eng Sci ; 380(2214): 20210116, 2022 Jan 10.
Article in English | MEDLINE | ID: covidwho-2262510
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'.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Randomized controlled trials Limits: Humans Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0116

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Randomized controlled trials Limits: Humans Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0116