  In this paper, we study a novel contact tracing query (CTQ) that finds users
who have been in $direct$ $contact$ with the query user or $in$ $contact$
$with$ $the$ $already$ $contacted$ $users$ in subsequent timestamps from a
large spatio-temporal database. The CTQ is of paramount importance in the era
of new COVID-19 pandemic world for finding possible list of potential COVID-19
exposed patients. A straightforward way to answer the CTQ is using traditional
spatio-temporal indexes. However, these indexes cannot serve the purpose as
each user covers a large area within the time-span of potential disease
spreading and thus they can hardly use efficient pruning techniques. We propose
a multi-level index, namely QR-tree, that consider both space coverage and the
co-visiting patterns to group users so that users who are likely to meet the
query user are grouped together. More specifically, we use a quadtree to
partition user movement traces w.r.t. space and time, and then exploit these
space-time mapping of user traces to group users using an R-tree. The QR-tree
facilitates efficient pruning and enables accessing only potential sets of user
who can be the candidate answers for the CTQ. Experiments with real datasets
show the effectiveness of our approach.
