  Influenza epidemics result in a public health and economic burden around the
globe. Traditional surveillance techniques, which rely on doctor visits,
provide data with a delay of 1-2 weeks. A means of obtaining real-time data and
forecasting future outbreaks is desirable to provide more timely responses to
influenza epidemics. In this work, we present the first implementation of a
novel data set by demonstrating its ability to supplement traditional disease
surveillance at multiple spatial resolutions. We use Internet traffic data from
the Centers for Disease Control and Prevention (CDC) website to determine the
potential usability of this data source. We test the traffic generated by ten
influenza-related pages in eight states and nine census divisions within the
United States and compare it against clinical surveillance data. Our results
yield $r^2$ = 0.955 in the most successful case, promising results for some
cases, and unsuccessful results for other cases. These results demonstrate that
Internet data may be able to complement traditional influenza surveillance in
some cases but not in others. Specifically, our results show that the CDC
website traffic may inform national and division-level models but not models
for each individual state. In addition, our results show better agreement when
the data were broken up by seasons instead of aggregated over several years. In
the interest of scientific transparency to further the understanding of when
Internet data streams are an appropriate supplemental data source, we also
include negative results (i.e., unsuccessful models). We anticipate that this
work will lead to more complex nowcasting and forecasting models using this
data stream.
