Covid-19 has radically changed our lives, with many governments and businesses mandating work-from-home (WFH) and remote education. However, work-from-home policy is not always known globally, and even when enacted, compliance can vary. These uncertainties suggest a need to measure WFH and confirm actual policy implementation. We show new algorithms that detect WFH from changes in network use during the day. We show that change-sensitive networks reflect mobile computer use, detecting WFH from changes in network intensity, the diurnal and weekly patterns of IP address response. Our algorithm provides new analysis of existing, continuous, global scans of most of the responsive IPv4 Internet (about 5.1M /24 blocks). Reuse of existing data allows us to study the emergence of Covid-19, revealing global reactions. We demonstrate the algorithm in networks with known ground truth, evaluate the data reconstruction and algorithm design choices with studies of real-world data, and validate our approach by testing random samples against news reports. In addition to Covid-related WFH, we also find other government-mandated lockdowns. Our results show the first use of network intensity to infer-real world behavior and policies.
翻译:Covid-19已经从根本上改变了我们的生活,许多政府和企业都要求从家到家工作(WFH)和远程教育,然而,从全球来看,从工作到家的政策并不总是为人所知,即使颁布后,遵守情况也会有所不同。这些不确定因素表明有必要测量WFH,并证实实际政策的执行情况。我们展示了新的算法,从网络使用当天网络的变化中检测到WFH。我们显示,对变化敏感的网络反映了移动计算机的使用,从网络强度的变化中检测到WFH,从IP地址反应的底线和每周模式中检测到IP地址的变化中检测到WFH。我们的算法对大多数反应灵敏的IPv4互联网(约5.1M /24块)的现有、连续的全球扫描提供了新的分析。利用现有数据使我们能够研究Covid-19的出现,揭示了全球反应。我们展示了已知地面真相的网络的算法,用真实数据研究来评估数据重建和算法设计选择,并通过对新闻报道进行随机抽样来验证我们的方法。除了与Covid有关的WFH以外,我们还发现了其他政府授权的锁定。我们的结果显示了网络在现实世界中首次使用强度政策和行为。