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Integrating Social Sciences to Mitigate Against Covid

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  • Additional Information
    • Contributors:
      Génétique fonctionnelle des maladies infectieuses - Functional Genetics of Infectious Diseases; Institut Pasteur Paris (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité); Pasteur Kyoto International Joint Research Unit for Integrative Vaccinomics Kyoto, Japan; Institut Pasteur Paris (IP)-Université Paris Cité (UPCité); Géographie-cités (GC (UMR_8504)); Université Paris 1 Panthéon-Sorbonne (UP1)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité); Centre de sciences humaines de New Delhi (CSH); Ministère de l'Europe et des Affaires étrangères (MEAE)-Centre National de la Recherche Scientifique (CNRS); Makoto Yano; Fumihiko Matsuda; Anavaj Sakuntabhai; Shigeru Hirota
    • Publication Information:
      HAL CCSD
      Springer Singapour
    • Publication Date:
      2022
    • Collection:
      Université Paris 1 Panthéon-Sorbonne: HAL
    • Abstract:
      International audience ; The SARS-CoV-2 pandemic has led to the implementation of unprecedented public health intervention measures, not least the lockdown of countries worldwide. In our hyperconnected world exemplified by social media, it is now possible to derive quantitative measures of human mobilities at useful spatial scales. In this chapter we discuss how the use of Facebook data enables us not only to capture the impact of lockdown on human mobility but also to assess how changes in mobility contribute to the spread of the virus. By performing a comparative analysis across four countries of differing levels of lockdown—Sweden, US, France and Colombia—we show that mobility contributes a substantial amount to the spread of the disease. This contribution is strongest when the local number of cases is low, but, importantly, is maintained even when the virus is widespread. Current epidemiological models do not take into account such mobility patterns and yet there exists a developed theoretical framework within which mobility can be included. Inclusion of mobility data would allow public health authorities to focus on highly connected hubs of infection and, because mobility patterns are relatively stable over time, would also enable forecasting of how the spread of this or another novel virus is going to occur. Anticipating epidemics and their spread is key for developing suitable but targeted intervention strategies and avoiding draconian lockdowns that are so harmful to the economy.
    • Relation:
      hal-03478099; https://hal.science/hal-03478099; https://hal.science/hal-03478099/document; https://hal.science/hal-03478099/file/Paul2022_Chapter_IntegratingSocialSciencesToMit.pdf
    • Accession Number:
      10.1007/978-981-16-5727-6_3
    • Online Access:
      https://hal.science/hal-03478099
      https://hal.science/hal-03478099/document
      https://hal.science/hal-03478099/file/Paul2022_Chapter_IntegratingSocialSciencesToMit.pdf
      https://doi.org/10.1007/978-981-16-5727-6_3
    • Rights:
      http://creativecommons.org/licenses/by-nc-nd/ ; info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.2D41AD2D