Statistics and Its Interface

Volume 14 (2021)

Number 1

The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method

Pages: 3 – 12

DOI: https://dx.doi.org/10.4310/20-SII634

Authors

Ting Tian (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Wenxiang Luo (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Jianbin Tan (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Yukang Jiang (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Minqiong Chen (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Wenliang Pan (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Songpan Yang (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Jiashu Zhao (School of Mathematics, Sun Yat-Sen University, Guangzhou, China)

Xueqin Wang (School of Statistics, Capital University of Economics and Business, Beijing, China; and School of Management, University of Science and Technology of China, Hefei, AH, China)

Heping Zhang (School of Public Health, Yale University, New Haven, Connecticut, U.S.A.)

Abstract

The outbreak of novel coronavirus disease (COVID-19) has spread around the world since it was detected in December 2019. The Chinese government executed a series of interventions to curb the pandemic. The “battle” against COVID-19 in Shenzhen, China is valuable because populated industrial cities are the epic centres of COVID-19 in many regions. We made use of synthetic control methods to create a reference population matching specific characteristics of Shenzhen. With both the synthetic and observed data, we introduced an epidemic compartmental model to compare the spread of COVID-19 between Shenzhen and its counterpart regions in the United States that didn’t implement interventions for policy evaluation. Once the effects of policy interventions adopted in Shenzhen were estimated, the delay effects of those interventions were referred to provide the further control degree of interventions. Thus, the hypothetical epidemic situations in Shenzhen were inferred by using time-varying reproduction numbers in the proposed SIHR (Susceptible, Infectious, Hospitalized, Removed) model and considering if the interventions were delayed by 0 day to 5 days. The expected cumulative confirmed cases would be 1546, which is 5.75 times of the observed cumulative confirmed cases of 269 in Shenzhen on February 3, 2020, based on the data from the counterpart counties (mainly from Broward, New York, Santa Clara, Pinellas, and Westchester) in the United States. If the interventions were delayed by 5 days from the day when the interventions started, the expected cumulative confirmed cases of COVID-19 in Shenzhen on February 3, 2020 would be 676 with 95% credible interval (303,1959). Early implementation of mild interventions can subdue the epidemic of COVID-19. The later the interventions were implemented, the more severe the epidemic was in the hard-hit areas. Mild interventions are less damaging to the society but can be effective when implemented early.

Keywords

mild interventions, causal effects, data assimilation, synthetic control methods, delay effects, time-varying reproduction numbers

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Dr. T. Tian, W. Luo, J. Tan and Y. Jiang contributed equally to this article.

Tian’s research is partially supported by National Natural Science Foundation of China (Grant No. 12001554). Wang’s research is partially supported by National Natural Science Foundation of China (Grant No. 71991474, Grant No. 11771462), and the Pearl River S&T Nova Program of Guangzhou (Grant No. 201806010142).

Received 16 July 2020

Accepted 30 August 2020

Published 18 December 2020