  COVID-19 has disrupted the global economy and well-being of people at an
unprecedented scale and magnitude. To contain the disease, an effective early
warning system that predicts the locations of outbreaks is of crucial
importance. Studies have shown the effectiveness of using large-scale mobility
data to monitor the impacts of non-pharmaceutical interventions (e.g.,
lockdowns) through population density analysis. However, predicting the
locations of potential outbreak occurrence is difficult using mobility data
alone. Meanwhile, web search queries have been shown to be good predictors of
the disease spread. In this study, we utilize a unique dataset of human
mobility trajectories (GPS traces) and web search queries with common user
identifiers (> 450K users), to predict COVID-19 hotspot locations beforehand.
More specifically, web search query analysis is conducted to identify users
with high risk of COVID-19 contraction, and social contact analysis was further
performed on the mobility patterns of these users to quantify the risk of an
outbreak. Our approach is empirically tested using data collected from users in
Tokyo, Japan. We show that by integrating COVID-19 related web search query
analytics with social contact networks, we are able to predict COVID-19 hotspot
locations 1-2 weeks beforehand, compared to just using social contact indexes
or web search data analysis. This study proposes a novel method that can be
used in early warning systems for disease outbreak hotspots, which can assist
government agencies to prepare effective strategies to prevent further disease
spread.

Social and Information Networks;Physics and Society