dc.contributor.author | Molefi, Mooketsi | |
dc.contributor.author | Tlhakanelo, John T. | |
dc.contributor.author | Phologolo, Thabo | |
dc.contributor.author | Hamda, Shimeles G. | |
dc.contributor.author | Masupe, Tiny | |
dc.contributor.author | Tsima, Billy | |
dc.contributor.author | Setlhare, Vincent | |
dc.contributor.author | Mashalla, Yohana | |
dc.contributor.author | Wiebe, Douglas J. | |
dc.date.accessioned | 2023-01-16T09:48:14Z | |
dc.date.available | 2023-01-16T09:48:14Z | |
dc.date.issued | 2021-10-28 | |
dc.identifier.citation | Molefi, M. et al. (2021) The impact of China’s lockdown policy on the incidence of CoVID-19: an interrupted time series analysis. BioMed Research International, Vol 2021, pp. 1-5 | en_US |
dc.identifier.issn | 2314-6133 (Print) | |
dc.identifier.issn | 2314-6141 (Online) | |
dc.identifier.uri | http://hdl.handle.net/10311/2478 | |
dc.description | NB: Some scientific formulas or symbols may not appear as they are on the original document | en_US |
dc.description.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 (). Comparing the period immediately following the intervention points to the counterfactual, there was a daily increase of 2,746% () versus a decline of 207% () 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 (). 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Hindawi, https://www.hindawi.com/journals/bmri/ | en_US |
dc.subject | China's lockdown policy | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | China | en_US |
dc.title | The Impact of China’s lockdown policy on the incidence of COVID-19: an interrupted time series analysis | en_US |
dc.type | Published Article | en_US |
dc.link | https://www.hindawi.com/journals/bmri/2021/9498029/#copyright | en_US |