C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak
Keywords:AI Responses to the COVID-19 Pandemic (Covid19), Mining of Spatial, Temporal or Spatio-Temporal Da, Internet of Things, Sensor Networks & Smart Cities, Transfer/Adaptation/Multi-task/Meta/Automated Learning
AbstractThe novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn “city-invariant” representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.
How to Cite
Xiao, C., Zhou, J., Huang, J., Zhuo, A., Liu, J., Xiong, H., & Dou, D. (2021). C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 4892-4900. https://doi.org/10.1609/aaai.v35i6.16622
AAAI Technical Track Focus Area on AI Responses to the COVID-19 Pandemic