Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
Changsha Center for Disease Control and Prevention, Changsha, 410005, Hunan, China.
J Infect Public Health. 2022 Jun;15(6):609-614. doi: 10.1016/j.jiph.2022.04.012. Epub 2022 Apr 29.
Despite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic.
Real-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen's slope.
In spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread.
Centralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.
尽管投入了大量资源来遏制 SARS-CoV-2 的传播，但控制 COVID-19 大流行一直是一个重大挑战。病毒的新变种不断出现，导致新的感染浪潮和重新引入控制措施。在这项研究中，我们评估了大流行早期实施的遏制策略的有效性。
从多个不同来源（包括 PubMed、MEDLINE、Facebook、疫情预测和 Google 移动性报告）检索了 2020 年 1 月 1 日至 5 月 1 日期间 COVID-19 病例的真实世界数据。我们分析了 18 个实施了遏制策略（如旅行限制、封锁、居家令、学校/公共活动关闭、社交距离和暴露史信息管理（数字接触者追踪，DCT））的国家/地区的数据。主要结局指标是在实施控制措施前 30 天和后 30 天新病例数的变化。我们还比较了集中式与分散式 DCT 的有效性。使用 Mann-Kendall（M-K）趋势检验分析 COVID-19 的时间序列数据，以调查这些措施对新病例数变化的影响。使用 M-K z 值和 Sen 斜率比较新病例数变化的速率。
尽管广泛实施了封锁、旅行限制、社交距离、学校关闭和居家令等常规策略，但分析表明，这些措施并不能阻止病毒的传播。然而，采用集中式数据存储的 DCT 的国家更有可能控制病毒的传播。
集中式 DCT 在控制 COVID-19 的传播方面更为有效。在未来的疫情爆发中，应考虑早期实施集中式 DCT。但是，需要解决公众接受度、数据安全和隐私问题等挑战。
