In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatio-temporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; February 10 - April 26, 2020), consisting of ~1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1%. The strongest reduction is linked to bans on gatherings of more than 5 people, which is estimated to have decreased mobility by 24.9%, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7-13 days ahead. A 1% reduction in human mobility predicts a 0.88-1.11% reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision-makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.
翻译:针对新的冠状病毒疾病(COVID-19),政府采取了对人类行为产生重大影响的严厉政策措施。在这里,我们对COVID-19流行病期间的人类流动性进行了大规模、时空分析。我们从全国范围的匿名、汇总的电信数据(瑞士;2020年2月10日至4月26日)中获得了人类流动性,由大约1.5亿次旅行组成。与2019年同期相比,瑞士的人类流动下降了49.1 % 。最大幅度的下降与禁止5人以上的集会有关,估计其流动性下降了24.9 %, 其次是关闭场地(商店、餐馆和酒吧)和学校关闭。因此,在某一天,人类流动性就预测了未来7-13天的情况。 人类流动性下降1%预测每日报告的COVID-19案例将减少0.88-1.11 % 。当管理流行病时,通过电信数据监测人类流动性可以以两种方式支持公共决策者。首先,它有助于评估政策影响;其次,它为近时间监控提供了可扩展的证据。