The Impact of China’s Lockdown Policy on the Incidence of COVID-19: An Interrupted Time Series Analysis

Abstract

Background. Policy changes are often necessary to contain the detrimental impact of epidemics such as those brought about by coronavirus disease (COVID-19). In the earlier phases of the emergence of COVID-19, China was the first to impose strict restrictions on movement (lockdown) on January 23rd, 2020. A strategy whose effectiveness in curtailing COVID-19 was yet to be determined. We, therefore, sought to study the impact of the lockdown in reducing the incidence of COVID-19. Methods. Daily cases of COVID-19 that occurred in China which were registered between January 12th and March 30th, 2020, were extracted from the Johns Hopkins CSSE team COVID-19 ArcGIS® dashboards. Daily cases reported were used as data points in the series. Two interrupted series models were run: one with an interruption point of 23 January 2020 (model 1) and the other with a 14-day deferred interruption point of 6th February (model 2). For both models, the magnitude of change (before and after) and linear trend analyses were measured, and β-coefficients reported with 95% confidence interval (CI) for the precision. Results. Seventy-eight data points were used in the analysis. There was an 11% versus a 163% increase in daily cases in models 1 and 2, respectively, in the preintervention periods ( p ≤ 0.001 ). Comparing the period immediately following the intervention points to the counterfactual, there was a daily increase of 2,746% ( p < 0.001 ) versus a decline of 207% ( p = 0.802 ) in model 2. However, in both scenarios, there was a statistically significant drop in the daily cases predicted for this data and beyond when comparing the preintervention periods and postintervention periods ( p < 0.001 ). Conclusion. There was a significant decrease the COVID-19 daily cases reported in China following the institution of a lockdown, and therefore, lockdown may be used to curtail the burden of COVID-19.

Use of interrupted Time Series Design in COVID-19 data of Nepal

“…It can be reflected that the government's decision to end the lockdown was possibly not appropriate at that time as the lockdown had controlled the COVID-19 transmission and the ending of the lockdown induced its abrupt increase. The findings of this study align with that of India 11 and China 12 both of which found that institutions of lockdown had significantly reduced the daily cases of COVID-19 and contrasts with the case of New Zealand which with early preparation and systematic management managed the COVID-19 cases to keep low without posing a longer lockdown period. 13 The Government of Nepal decided to impose the second lockdown on 29 th April 2021 which ended after four months on the first of September.…”

Spatial and Temporal Analysis of COVID-19 Cases in West Java, Indonesia and Its Influencing Factors

“…However, this must be cautiously inferred as a limited testing and reporting capacity for COVID-19 in Indonesia may have contributed to the lower incidence [ 21 , 22 , 23 ]. Advanced countries such as the USA had more RNA molecular testing facilities, while countries with full lockdowns such as China and New Zealand [ 24 , 25 , 26 , 27 ] showed very low COVID-19 incidence and fatalities [ 20 ].…”

Effects of Closures and Openings on Public Health in the Time of COVID-19: A Cross-Country and Temporal Trend Analysis

“…Our trend analysis method is illustrated in Figure 1. In each panel of this figure, we include three separate temporal periods: (1) the period before a public health intervention where data are used to evaluate a possible ''Business-as-Usual (BAU)'' scenario; (2) the period after an intervention, but before it has an impact on public health performance, or a lag in impacts (e.g., see Caristia et al, 2020;Fang et al, 2020;Ghosal et al, 2020;Guzzetta et al, 2021;Molefi et al, 2021;Siedner et al, 2020), what we refer to as a ''Yet-to-be-Impact'' period; and (3) the period after the intervention that coincides with when there is an impact on public health performance, what we refer to as an ''Observed Impact'' period.…”