Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment Using Epidemiologic Data From 145 Countries



Most countries have adopted public activity intervention policies to control the coronavirus disease 2019 (COVID-19) pandemic. Nevertheless, empirical evidence of the effectiveness of different interventions on the containment of the epidemic was inconsistent.


We retrieved time-series intervention policy data for 145 countries from the Oxford COVID-19 Government Response Tracker from December 31, 2019, to July 1, 2020, which included 8 containment and closure policies. We investigated the association of timeliness, stringency, and duration of intervention with cumulative infections per million population on July 1, 2020. We introduced a novel counterfactual estimator to estimate the effects of these interventions on COVID-19 time-varying reproduction number (R ).


There is some evidence that earlier implementation, longer durations, and more strictness of intervention policies at the early but not middle stage were associated with reduced infections of COVID-19. The counterfactual model proved to have controlled for unobserved time-varying confounders and established a valid causal relationship between policy intervention and R reduction. The average intervention effect revealed that all interventions significantly decrease R after their implementation. R decreased by 30% (22%-41%) in 25 to 32 days after policy intervention. Among the 8 interventions, school closing, workplace closing, and public events cancellation demonstrated the strongest and most consistent evidence of associations.


Our study provides more reliable evidence of the quantitative effects of policy interventions on the COVID-19 epidemic and suggested that stricter public activity interventions should be implemented at the early stage of the epidemic for improved containment.


Jichao Sun Yefeng Zheng Wenhua Liang Zifeng Yang Zhiqi Zeng Tiegang Li Junjie Luo Man Tat Alexander Ng Jianxing He Nanshan Zhong

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