RAPID: Internet Traffic and Compliance with Government Stay-at-Home Measures

点击翻译按钮获取中文摘要

Public health measures such as quarantine, stay-at-home and shelter-at-home are considered effective ways to limit the spread of infectious diseases. This research effort is motivated by a simple observation - as citizens comply with such measures and move to e-learning and to work remotely, one expects to see significant changes in daily network traffic patterns. This project explores the potential for using such changes as a privacy-preserving proxy of citizen compliance with stay-at-home-like measures. This effort will leverage the vantage point of a global content distribution network (CDN) service to understand changes on demand before and after official lockdowns or stay-at-home policies take effect at different locales. To understand the relation between these public health measures and network traffic, it will rely on natural experiments, in which users in a region in a treated group (where public health measures are in place) will be matched with similar users in an untreated group, in order to effectively assess compliance and network traffic changes across geographic regions. This research will enable understanding how state-at-home measures are working and characterizing the degree of compliance by observing network traffic demand in different regions over time, while preserving privacy. The results from this work will help provide public health authorities with a basis for an evidence-based analysis of their effectiveness. The tools developed and the derived data will be made available to the community. All research products will be made available at the following website http://aqualab.cs.northwestern.edu/projects/elpis/ The site will remain accessible for at least a year after the completion of the work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.