The Effect of Governmental Health Measures on Public Behaviour During the COVID-19 Pandemic Outbreak

Document Type : Original Article

Authors

1 School of Communication, Soochow University, Suzhou, China

2 Health Inspection Institute, Health Commission of Suzhou, Suzhou, China

3 Jiangsu Key Laboratory of Culture and Tourism for Digital Twin Perception Technology in Museums, Suzhou, China

Abstract

Background 
The coronavirus disease 2019 (COVID-19) pandemic resulted in radical changes in many aspects of life. To deal with this, each country has implemented continuous health measures from the beginning of the outbreak. Discovering how governmental actions impacted public behaviour during the outbreak stage is the purpose of this study.

Methods 
This study uses a hybrid large-scale data visualisation method to analyse public behaviour (epidemic concerns, self- protection, and mobility trends), using the data provided by multiple authorities. Meanwhile, a content analysis method is used to qualitatively code the health measures of three countries with severe early epidemic outbreaks from different continents, namely China, Italy, and the United States. Eight dimensions are coded to rate the mobility restrictions implemented in the above countries.

Results 
(1) Governmental measures did not immediately persuade the public to change their behaviours during the COVID-19 epidemic. Instead, the public behaviour proceeded in a three-phase rule, which is typically witnessed in an epidemic outbreak, namely the wait-and-see phase, the surge phase and the slow-release phase. (2) The strictness of the mobility restrictions of the three countries can be ranked as follows: Hubei Province in China (with an average score of 8.5 out of 10), Lombardy in Italy (7.125), and New York State in the United States (5.375). Strict mobility restrictions are more likely to cause a surge of population outflow from the epidemic area in the short term, whereas the effect of mobility restrictions is positively related to the stringency of policies in the long term.

Conclusion 
The public showed generally lawful behaviour during regional epidemic outbreaks and blockades. Meanwhile public behaviour was deeply affected by the actions of local governments, rather than the global pandemic situation. The contextual differences between the various countries are important factors that influence the effects of the different governments’ health measures.

Keywords

Main Subjects


  1. COVID-19 Dashboards. 2021; https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6. Accessed July 29, 2021.
  2. Cheng C, Barceló J, Hartnett AS, Kubinec R, Messerschmidt L. COVID-19 government response event dataset (CoronaNet v.1.0). Nat Hum Behav. 2020;4(7):756-768. doi:1038/s41562-020-0909-7
  3. Uddin S, Imam T, Moni MA, Thow AM. Onslaught of COVID-19: how did governments react and at what point of the crisis? Popul Health Manag. 2021;24(1):13-19. doi:1089/pop.2020.0138
  4. Hale T, Petherick A, Phillips T, Webster S. Variation in government responses to COVID-19. Blavatnik School of Government Working Paper. 2020;31:2020-2011. https://www.bsg.ox.ac.uk/sites/default/files/2020-09/BSG-WP-2020-032-v7.0.pdf
  5. Sebastiani G, Massa M, Riboli E. COVID-19 epidemic in Italy: evolution, projections and impact of government measures. Eur J Epidemiol. 2020;35(4):341-345. doi:1007/s10654-020-00631-6
  6. Fang Y, Nie Y, Penny M. Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: a data-driven analysis. J Med Virol. 2020;92(6):645-659. doi:1002/jmv.25750
  7. Gatto M, Bertuzzo E, Mari L, et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc Natl Acad Sci U S A. 2020;117(19):10484-10491. doi:1073/pnas.2004978117
  8. Yang Z, Zeng Z, Wang K, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis. 2020;12(3):165-174. doi:21037/jtd.2020.02.64
  9. Haug N, Geyrhofer L, Londei A, et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nat Hum Behav. 2020;4(12):1303-1312. doi:1038/s41562-020-01009-0
  10. Kirsch TD, Moseson H, Massaquoi M, et Impact of interventions and the incidence of Ebola virus disease in Liberia-implications for future epidemics. Health Policy Plan. 2017;32(2):205-214. doi:10.1093/heapol/czw113
  11. Roma P, Monaro M, Muzi L, et al. How to improve compliance with protective health measures during the COVID-19 outbreak: testing a moderated mediation model and machine learning algorithms. Int J Environ Res Public Health. 2020;17(19):7252. doi:3390/ijerph17197252
  12. Finset A, Bosworth H, Butow P, et al. Effective health communication - a key factor in fighting the COVID-19 pandemic. Patient Educ Couns. 2020;103(5):873-876. doi:1016/j.pec.2020.03.027
  13. Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? a meta-analysis of the experimental evidence. Psychol Bull. 2006;132(2):249-268. doi:1037/0033-2909.132.2.249
  14. Rhodes RE, Dickau L. Experimental evidence for the intention-behavior relationship in the physical activity domain: a meta-analysis. Health Psychol. 2012;31(6):724-727. doi:1037/a0027290
  15. Siewe Fodjo JN, Pengpid S, de Moura Villela EF, et al. Mass masking as a way to contain COVID-19 and exit lockdown in low- and middle-income countries. J Infect. 2020;81(3):e1-e5. doi:1016/j.jinf.2020.07.015
  16. Muto K, Yamamoto I, Nagasu M, Tanaka M, Wada K. Japanese citizens' behavioral changes and preparedness against COVID-19: an online survey during the early phase of the pandemic. PLoS One. 2020;15(6):e0234292. doi:1371/journal.pone.0234292
  17. Zhong BL, Luo W, Li HM, et al. Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey. Int J Biol Sci. 2020;16(10):1745-1752. doi:7150/ijbs.45221
  18. Alomari E, Katib I, Albeshri A, Mehmood R. COVID-19: detecting government pandemic measures and public concerns from Twitter Arabic data using distributed machine learning. Int J Environ Res Public Health. 2021;18(1):282. doi:3390/ijerph18010282
  19. Wong SH, Teoh JYC, Leung CH, et al. COVID-19 and public interest in face mask use. Am J Respir Crit Care Med. 2020;202(3):453-455. doi:1164/rccm.202004-1188LE
  20. Weible CM, Nohrstedt D, Cairney P, et al. COVID-19 and the policy sciences: initial reactions and perspectives. Policy Sci. 2020;53(2):225-241. doi:1007/s11077-020-09381-4
  21. McGuire WJ. Persuasion, resistance, and attitude change. In: de Sola Pool I, Frey FW, Schramm W, Parker EB, Maccoby N, eds. Handbook of Communication. Chicago: Rand McNally; 1973.
  22. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51(3):390-395. doi:1037//0022-006x.51.3.390
  23. Uddin S, Imam T, Ali Moni M. The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate. Paper presented at: 2021 Australasian Computer Science Week Multiconference; 2021.
  24. Brzezinski A, Deiana G, Kecht V, Van Dijcke D. The COVID-19 Pandemic: Government vs. Community Action Across the United States. Covid Economics: Vetted and Real-Time Papers. 2020;7:115-156.
  25. Branson C, Duffy B, Perry C, Wellings D. Acceptable Behaviour: Public Opinion on Behaviour Change Policy. London: Ipsos MORI; 2012.
  26. Taghrir MH, Akbarialiabad H, Ahmadi Marzaleh M. Efficacy of mass quarantine as leverage of health system governance during COVID-19 outbreak: a mini policy review. Arch Iran Med. 2020;23(4):265-267. doi:34172/aim.2020.08
  27. Shi Q, Hu Y, Peng B, et al. Effective control of SARS-CoV-2 transmission in Wanzhou, China. Nat Med. 2021;27(1):86-93. doi:1038/s41591-020-01178-5
  28. Vasconcelos GL, Macêdo AMS, Ospina R, et al. Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies. PeerJ. 2020;8:e9421. doi:7717/peerj.9421
  29. Landoni G, Maimeri N, Fedrizzi M, et al. Why are Asian countries outperforming the Western world in controlling COVID-19 pandemic? Pathog Glob Health. 2021;115(1):70-72. doi:1080/20477724.2020.1850982
  30. Olagnier D, Mogensen TH. The COVID-19 pandemic in Denmark: big lessons from a small country. Cytokine Growth Factor Rev. 2020;53:10-12. doi:1016/j.cytogfr.2020.05.005
  31. Uddin S, Imam T, Khushi M, Khan A, Ali M. How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? lessons for pandemic management. Pers Individ Dif. 2021;175:110692. doi:1016/j.paid.2021.110692
  32. Lazarus JV, Binagwaho A, El-Mohandes AAE, et al. Keeping governments accountable: the COVID-19 assessment scorecard (COVID-SCORE). Nat Med. 2020;26(7):1005-1008. doi:1038/s41591-020-0950-0
  33. Alam F, Almaghthawi A, Katib I, Albeshri A, Mehmood R. IResponse: an AI and IoT-enabled framework for autonomous COVID-19 pandemic management. Sustainability. 2021;13(7):3797. doi:3390/su13073797
Volume 11, Issue 10
October 2022
Pages 2166-2174
  • Receive Date: 20 April 2021
  • Revise Date: 16 August 2021
  • Accept Date: 08 September 2021
  • First Publish Date: 11 September 2021