  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.
