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

Nowcasting Influenza Incidence with CDC Web Traffic Data: A Demonstration Using a Novel Data Set