Center for Health Systems Innovation, Department of Management Science and Information Systems, Oklahoma State University, Tulsa, OK, United States.
School of Management, Sabanci University, Istanbul, Turkey.
JMIR Public Health Surveill. 2020 May 28;6(2):e19862. doi: 10.2196/19862.
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.
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.
Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) 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 β values.
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.
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.
在大流行时期尚无治愈方法的情况下，社交隔离措施似乎是减缓疾病传播最有效的干预措施。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隔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%到超过 90%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。但是，真实的交易数据可以减少不确定性，并提供社交隔离效果的干扰较小的图景。
本文的目的是整合多个交易数据集（来自 Google 和 Apple 的 GPS 移动数据以及欧洲疾病预防控制中心的疾病统计数据），以研究社交隔离政策在 26 个国家/地区的作用，并分析冠状病毒病（COVID-19）大流行的传播率在 5 周内的情况。
依靠易感-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/地区 COVID-19 的每周传播率（β），连续 5 周。然后，我们将这些数据与 Google 和 Apple 移动数据集集成在一起，并在同一时间段内使用机器学习方法来研究移动因素与β值之间的关系。
梯度提升树回归分析表明，社交隔离政策导致的移动模式变化解释了疾病传播率变化的约 47％。
与基于模拟的研究一致，真实的跨国交易数据证实了社交隔离干预措施在减缓 COVID-19 传播方面的有效性。除了为社交隔离的想法提供了干扰较小且更具普遍性的支持外，我们还为公共卫生政策制定者提供了有关应优先考虑实施社交隔离措施的特定位置的见解。
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