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Spatial super-spreaders and super-susceptibles in human movement networks

Scientific reports, 2020-10, Vol.10 (1), p.18642-18642, Article 18642 [Peer Reviewed Journal]

The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-020-75697-z ;PMID: 33122721

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  • Title:
    Spatial super-spreaders and super-susceptibles in human movement networks
  • Author: Chin, Wei Chien Benny ; Bouffanais, Roland
  • Subjects: Carrier State ; Central business districts ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; COVID-19 - transmission ; COVID-19 - virology ; Disease spread ; Epidemiology ; Humans ; Movement ; SARS-CoV-2 - isolation & purification ; Singapore - epidemiology ; Social Isolation ; Transportation ; Urban environments ; Urban planning
  • Is Part Of: Scientific reports, 2020-10, Vol.10 (1), p.18642-18642, Article 18642
  • Description: As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be "super-spreaders". Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-020-75697-z
    PMID: 33122721
  • Source: PubMed (Medline)
    AUTh Library subscriptions: ProQuest Central
    MEDLINE
    Coronavirus Research Database
    DOAJ Directory of Open Access Journals
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