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Predicted Impact of the Lockdown Measure in Response to Coronavirus Disease 2019 (COVID-19) in Greater Bangkok, Thailand, 2021

Author

Listed:
  • Sonvanee Uansri

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Titiporn Tuangratananon

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand
    Bureau of Health Promotion, Department of Health, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Mathudara Phaiyarom

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Nattadhanai Rajatanavin

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Rapeepong Suphanchaimat

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand
    Division of Epidemiology, Department of Disease Control, Ministry of Public Health, Nonthaburi 11000, Thailand)

  • Warisara Jaruwanno

    (International Health Policy Programme, Ministry of Public Health, Nonthaburi 11000, Thailand)

Abstract

In mid-2021, Thailand faced a fourth wave of Coronavirus Disease 2019 (COVID-19) predominantly fueled by the Delta and Alpha variants. The number of cases and deaths rose exponentially, alongside a sharp increase in hospitalizations and intubated patients. The Thai Government then implemented a lockdown to mitigate the outbreak magnitude and prevent cases from overwhelming the healthcare system. This study aimed to model the severity of the outbreak over the following months by different levels of lockdown effectiveness. Secondary analysis was performed on data primarily obtained from the Ministry of Health; the data were analyzed using both the deterministic compartmental model and the system dynamics model. The model was calibrated against the number of daily cases in Greater Bangkok during June–July 2021. We then assessed the outcomes (daily cases, daily deaths, and intubated patients) according to hypothetical lockdowns of varying effectiveness and duration. The findings revealed that lockdown measures could reduce and delay the peak of COVID-19 cases and deaths. A two-month lockdown with 60% effectiveness in the reduction in reproduction number caused the lowest number of cases, deaths, and intubated patients, with a peak about one-fifth of the size of a no-lockdown peak. The two-month lockdown policy also delayed the peak until after December, while in the context of a one-month lockdown, cases peaked during the end of September to early December (depending on the varying degrees of lockdown effectiveness in the reduction in reproduction number). In other words, the implementation of a lockdown policy did not mean the end of the outbreak, but it helped delay the peak. In this sense, implementing a lockdown helped to buy time for the healthcare system to recover and better prepare for any future outbreaks. We recommend further studies that explore the impact of lockdown measures at a sub-provincial level, and examine the impact of lockdowns on parameters not directly related to the spread of disease, such as quality of life and economic implications for individuals and society.

Suggested Citation

  • Sonvanee Uansri & Titiporn Tuangratananon & Mathudara Phaiyarom & Nattadhanai Rajatanavin & Rapeepong Suphanchaimat & Warisara Jaruwanno, 2021. "Predicted Impact of the Lockdown Measure in Response to Coronavirus Disease 2019 (COVID-19) in Greater Bangkok, Thailand, 2021," IJERPH, MDPI, vol. 18(23), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12816-:d:695530
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    References listed on IDEAS

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    1. Ashraf, Badar Nadeem, 2020. "Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    2. Mohammad Reza Davahli & Waldemar Karwowski & Redha Taiar, 2020. "A System Dynamics Simulation Applied to Healthcare: A Systematic Review," IJERPH, MDPI, vol. 17(16), pages 1-27, August.
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    Cited by:

    1. Weiwei Zhang & Shiyong Liu & Nathaniel Osgood & Hongli Zhu & Ying Qian & Peng Jia, 2023. "Using simulation modelling and systems science to help contain COVID‐19: A systematic review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 207-234, January.
    2. Jin-Young Won & Yu-Rim Lee & Myeong-Heum Cho & Yoon-Tae Kim & Bo-Young Heo, 2022. "Impact of Government Intervention in Response to Coronavirus Disease 2019," IJERPH, MDPI, vol. 19(23), pages 1-14, December.
    3. Sonvanee Uansri & Watinee Kunpeuk & Sataporn Julchoo & Pigunkaew Sinam & Mathudara Phaiyarom & Rapeepong Suphanchaimat, 2023. "Perceived Barriers of Accessing Healthcare among Migrant Workers in Thailand during the Coronavirus Disease 2019 (COVID-19) Pandemic: A Qualitative Study," IJERPH, MDPI, vol. 20(10), pages 1-11, May.

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