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Effects of transport-related COVID-19 policy measures: A case study of six developed countries.
Zhang, Junyi; Zhang, Runsen; Ding, Hongxiang; Li, Shuangjin; Liu, Rui; Ma, Shuang; Zhai, Baoxin; Kashima, Saori; Hayashi, Yoshitsugu.
  • Zhang J; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Zhang R; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Ding H; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Li S; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Liu R; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Ma S; Research Center for Advanced Science and Technology, The University of Tokyo, Japan.
  • Zhai B; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
  • Kashima S; College of Architecture and Urban Planning, Tongji University, China; Graduate School of Advanced Science and Engineering, Hiroshima University, Japan.
  • Hayashi Y; Graduate School of Advanced Science and Engineering, Graduate School for International Development and Cooperation, Hiroshima University, Japan.
Transp Policy (Oxf) ; 110: 37-57, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1253700
ABSTRACT
This study attempts to provide scientifically-sound evidence for designing more effective COVID-19 policies in the transport and public health sectors by comparing 418 policy measures (244 are transport measures) taken in different months of 2020 in Australia, Canada, Japan, New Zealand, the UK, and the US. The effectiveness of each policy is measured using nine indicators of infections and mobilities corresponding to three periods (i.e., one week, two weeks, and one month) before and after policy implementation. All policy measures are categorized based on the PASS approach (P prepare-protect-provide; A avoid-adjust; S shift-share; S substitute-stop). First, policy effectiveness is compared between policies, between countries, and over time. Second, a dynamic Bayesian multilevel generalized structural equation model is developed to represent dynamic cause-effect relationships between policymaking, its influencing factors and its consequences, within a unified research framework. Third, major policy measures in the six countries are compared. Finally, findings for policymakers are summarized and extensively discussed.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Case report / Experimental Studies Language: English Journal: Transp Policy (Oxf) Year: 2021 Document Type: Article Affiliation country: J.tranpol.2021.05.013

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Case report / Experimental Studies Language: English Journal: Transp Policy (Oxf) Year: 2021 Document Type: Article Affiliation country: J.tranpol.2021.05.013