  Given its wide-ranging and long-lasting impacts, COVID-19, especially its
spatial spreading dynamics has received much attention. Knowledge of such
dynamics helps public health professionals and city managers devise and deploy
efficient contact-tracing and treatment measures. However, most existing
studies focus on aggregate mobility flows and have rarely exploited the widely
available disaggregate-level human mobility data. In this paper, we propose a
Personalized PageRank (PPR) method to estimate COVID-19 transmission risks
based on a bipartite network of people and locations. The method incorporates
both mobility patterns of individuals and their spatiotemporal interactions. To
validate the applicability and relevance of the proposed method, we examine the
interplay between the spread of COVID-19 cases and intra-city mobility patterns
in a small synthetic network and a real-world mobility network from Hong Kong,
China based on transit smart card data. We compare the recall (sensitivity),
accuracy, and Spearmans correlation coefficient between the estimated
transmission risks and number of actual cases based on various mass tracing and
testing strategies, including PPR-based, PageRank (PR)-based, location-based,
route-based, and base case (no strategy). The results show that the PPR-based
method achieves the highest efficiency, accuracy, and Spearmans correlation
coefficient with the actual case number. This demonstrates the value of PPR for
transmission risk estimation and the importance of incorporating individual
mobility patterns for efficient contact-tracing and testing.
