  The 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.
