No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.
No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.
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DOI：
10.2196/19862
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发表时间：
2020-05-28
影响因子：
8.5
通讯作者：
Davazdahemami, Behrooz
中科院分区：
文献类型：
--
作者：
Delen, Dursun;Eryarsoy, Enes;Davazdahemami, Behrooz
背景技术背景：在大流行期间没有治愈方法的情况下，社交距离措施似乎是减缓疾病传播的最有效干预措施。已经进行了各种基于模拟的研究来调查这些措施的有效性。虽然这些研究一致证实了社交距离对疾病传播的缓解作用，但报告的有效性从感染人数减少10%到90%以上不等。这种程度的不确定性主要是由于流行病的复杂动态及其随时间变化的参数。然而，真实的交易数据可以减少不确定性，并提供一个更少的噪音图片的有效性，社会距离。目的：本文的目的是集成多个事务数据集（来自谷歌和苹果的GPS移动数据以及来自欧洲疾病预防和控制中心的疾病统计数据）研究26个国家的社交距离政策的作用，并分析冠状病毒疾病的传播率（COVID-19）大流行的5周过程中。依靠易感染-恢复（SIR）模型和官方COVID-19报告，我们首先计算了26个国家连续5周的COVID-19每周传播率（beta）。然后，我们将这些数据与谷歌和苹果的移动数据集在同一时间框架内整合，并使用机器学习方法来研究移动因素和beta值之间的关系。结果：梯度提升树回归分析显示，社交距离政策导致的移动模式变化解释了大约47%的疾病传播率变化。与基于模拟的研究一致，真实的跨国交易数据证实了社交距离干预措施在减缓COVID-19传播方面的有效性。除了为社交距离的想法提供更少的噪音和更普遍的支持外，我们还为公共卫生政策制定者提供了关于应优先执行社交距离措施的地点的具体见解。
BACKGROUND: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing.OBJECTIVE: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks.METHODS: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (beta) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and beta values.RESULTS: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates.CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.