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People’s responses to the COVID-19 pandemic during its early stages and factors affecting those responses

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  • Junyi Zhang

    (Hiroshima University)

Abstract

The world has suffered from the COVID-19 pandemic. While it is expected that societies will learn lessons from this experience, knowledge about how people responded to the pandemic in its early stages is very limited. With the aim of urgently providing policymakers with scientific evidence about how to better inform the public about fighting against COVID-19, this study made an initial attempt to assess how people responded to the COVID-19 outbreak during its early stages. Based on a life-oriented approach, this study collected data on a large set of behaviors and attitudes through a nationwide retrospective panel survey conducted in Japan at the end of March 2020, when the country had 1953 confirmed infection cases in total. Valid data were collected from 1052 residents from the whole of Japan, taking into account a balanced population distribution in terms of age, gender, and region. Respondents were asked to report changes in their daily activity-travel behavior, long-distance trips, and other life activities caused by the COVID-19 pandemic and associated factors (information reliability, risk perception, attitudes about policy-making and communications with the public, etc.). Results of both aggregate and modeling analyses (using a structural equation model and a data mining approach) indicate that poor communication with the public may have been closely related to the spread of COVID-19 in Japan, and that effective interventions should be made by focusing on interactions between target persons and close members of their social networks. It is also revealed that differentiated communications are necessary to encourage different types of behavioral changes. Risk communication should be better designed to encourage people to voluntarily modify their needs in life [L] and perform the needed activities [A] at places with sufficient spaces [S] and proper duration of time and at the proper timing [TING]. Such a LASTING approach may be crucial to enhance the effects of massive public involvement in mitigating the spread of COVID-19. The findings from this study are not only useful to tackle the current pandemic, but also have a long-term value for addressing future pandemics.

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  • Junyi Zhang, 2021. "People’s responses to the COVID-19 pandemic during its early stages and factors affecting those responses," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00720-1
    DOI: 10.1057/s41599-021-00720-1
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    References listed on IDEAS

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    1. Robert West & Susan Michie & G. James Rubin & Richard Amlôt, 2020. "Applying principles of behaviour change to reduce SARS-CoV-2 transmission," Nature Human Behaviour, Nature, vol. 4(5), pages 451-459, May.
    2. Zhang, Junyi, 2014. "Revisiting residential self-selection issues: A life-oriented approach," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(3), pages 29-45.
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    3. Beck, Matthew J. & Hensher, David A., 2022. "Australia 6 months after COVID-19 restrictions- part 1: Changes to travel activity and attitude to measures," Transport Policy, Elsevier, vol. 128(C), pages 286-298.
    4. Xin, Mengwei & Shalaby, Amer & Feng, Shumin & Zhao, Hu, 2021. "Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method," Transport Policy, Elsevier, vol. 111(C), pages 1-16.
    5. Ding, Hongxiang & Zhang, Junyi, 2021. "Dynamic associations between temporal behavior changes caused by the COVID-19 pandemic and subjective assessments of policymaking: A case study in Japan," Transport Policy, Elsevier, vol. 110(C), pages 58-70.
    6. Baining Zhao & Xuzhe Wang & Tianyu Zhang & Rongye Shi & Fengli Xu & Fanhang Man & Erbing Chen & Yang Li & Yong Li & Tao Sun & Xinlei Chen, 2024. "Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    7. Wang, Kaili & Liu, Yicong & Mashrur, Sk Md & Loa, Patrick & Habib, Khandker Nurul, 2021. "COVid-19 influenced households’ Interrupted Travel Schedules (COVHITS) survey: Lessons from the fall 2020 cycle," Transport Policy, Elsevier, vol. 112(C), pages 43-62.
    8. Varameth Vichiensan & Yoshitsugu Hayashi & Sudarat Kamnerdsap, 2021. "COVID-19 Countermeasures and Passengers’ Confidence of Urban Rail Travel in Bangkok," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    9. Kelsie Cassell & Casey M. Zipfel & Shweta Bansal & Daniel M. Weinberger, 2022. "Trends in non-COVID-19 hospitalizations prior to and during the COVID-19 pandemic period, United States, 2017–2021," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    10. Loa, Patrick & Hossain, Sanjana & Mashrur, Sk. Md. & Liu, Yicong & Wang, Kaili & Ong, Felita & Habib, Khandker Nurul, 2021. "Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area," Transport Policy, Elsevier, vol. 110(C), pages 71-85.
    11. Lei, Yiyuan & Ozbay, Kaan, 2021. "A robust analysis of the impacts of the stay-at-home policy on taxi and Citi Bike usage: A case study of Manhattan," Transport Policy, Elsevier, vol. 110(C), pages 487-498.

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