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The Effects of Stringent and Mild Interventions for Coronavirus Pandemic

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  • Ting Tian
  • Jianbin Tan
  • Wenxiang Luo
  • Yukang Jiang
  • Minqiong Chen
  • Songpan Yang
  • Canhong Wen
  • Wenliang Pan
  • Xueqin Wang

Abstract

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic’s spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (p-value:

Suggested Citation

  • Ting Tian & Jianbin Tan & Wenxiang Luo & Yukang Jiang & Minqiong Chen & Songpan Yang & Canhong Wen & Wenliang Pan & Xueqin Wang, 2021. "The Effects of Stringent and Mild Interventions for Coronavirus Pandemic," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 481-491, April.
    • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:534:p:481-491
    DOI: 10.1080/01621459.2021.1897015
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    Cited by:

    1. Jianbin Tan & Ye Shen & Yang Ge & Leonardo Martinez & Hui Huang, 2023. "Age‐related model for estimating the symptomatic and asymptomatic transmissibility of COVID‐19 patients," Biometrics, The International Biometric Society, vol. 79(3), pages 2525-2536, September.
    2. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    3. Cerqueti, Roy & Ficcadenti, Valerio, 2022. "Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    4. Wang, Zongrun & Zhou, Ling & Mi, Yunlong & Shi, Yong, 2022. "Measuring dynamic pandemic-related policy effects: A time-varying parameter multi-level dynamic factor model approach," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    5. Ramalingam Shanmugam & Lawrence Fulton & Jose Betancourt & Gerardo J. Pacheco & Keya Sen, 2023. "Indexing of US Counties with Overdispersed Incidences of COVID-19 Deaths," Mathematics, MDPI, vol. 11(14), pages 1-11, July.

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