  For epidemics control and prevention, timely insights of potential hot spots
are invaluable. Alternative to traditional epidemic surveillance, which often
lags behind real time by weeks, big data from the Internet provide important
information of the current epidemic trends. Here we present a methodology,
ARGOX (Augmented Regression with GOogle data CROSS space), for accurate
real-time tracking of state-level influenza epidemics in the United States.
ARGOX combines Internet search data at the national, regional and state levels
with traditional influenza surveillance data from the Centers for Disease
Control and Prevention, and accounts for both the spatial correlation structure
of state-level influenza activities and the evolution of people's Internet
search pattern. ARGOX achieves on average 28\% error reduction over the best
alternative for real-time state-level influenza estimation for 2014 to 2020.
ARGOX is robust and reliable and can be potentially applied to track county-
and city-level influenza activity and other infectious diseases.
