The Effects of Stringent and Mild Interventions for Coronavirus Pandemic

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: <0.001) and mild interventions (p-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, then epidemic would be under control effectively and early. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Keywords:

• Compartmental model
• Counterfactual
• COVID-19
• Synthetic control method
• Treatment effect

Supplementary Materials

In the supplementary materials, we provide more details about the procedure of SCM and the state space compartmental model, along with additional supporting evidence with results summarized in tables and figures.

Acknowledgments

The authors thank the editor, the associate editor, and three anonymous reviewers for valuable comments that have remarkably improved the manuscript.

Additional information

Funding

Tian’s research is partially supported by the National Natural Science Foundation of China (Grant No. 12001554) and the Natural Science Foundation of Guangdong Province, China (Grant No. 2020A1515010617). Wen’s research is partially supported by the National Natural Science Foundation of China (Grant No. 11801540), Natural Science Foundation of Guangdong (Grant No. 2017A030310572) and Fundamental Research Funds for the Central Universities (Grant No. WK2040170015, WK2040000016). Pan’s research is partially supported by the National Natural Science Foundation of China (Grant No. 12071494). Wang’s research is partially supported by the National Natural Science Foundation of China (Grant No. 71991474, Grant No. 11771462) the Science and Technology Program of Guangzhou, China (Grant No. 202002030129), and the Pearl River S&T Nova Program of Guangzhou (Grant No. 201806010142).