Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset

Abstract

<jats:sec> <jats:title>Background</jats:title> <jats:p>Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream.</jats:p> </jats:sec>

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

“…Our search identified 10 articles addressing internet search queries or webpage views. 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 Four articles studied Baidu, a popular internet search engine in China. 38 , 39 , 41 , 44 Three of these provided data on both correlative value and timeliness.…”

Assessing health human resource structure at Urumqi’s center for disease control and prevention

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Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

“…PHI is an imperfect source of data with its own biases such that its content and frequency of posting are not equally distributed across the population. For example, a recent secondary analysis of survey data revealed that women were more likely to post on COVID-19 than men; that black, Latino, and other non-white males were more likely to post on the topic than whites, and that people age 65 and above were more likely to post than younger people [ 68 ]. However, existing analysis tools take this into account, and use the frequency of posting as a variable in and of itself.…”

“…Spearman ( [11,18,31,33,39,54]) Pearson ( [19,20,37,38,43,44,48,49,55,60]) unspecified ( [12,22,25,30,50,51,61]) FDR [23], DBNM [24], KLD [47], JI [51], GCt [53], DFt [53], ARIMAX [55], ESA [57] unspecified ([13,14,30,35,38,43,49,50,61,62]) SARIMA [17], SDA [17], LiR [17], BCP [28], SH-ESD [42], STL [54] unspecified [48] SVM ([15,16,27,35,36,38,44,49,53]) LR ( [35,46,55]) NB ( [36,42] tributed across the population. For example, a recent secondary analysis of survey data revealed that women were more likely to post on COVID-19 than men; that black, Latino, and other non-white males were more likely to post on the topic than whites, and that people age 65 and above were more likely to post than younger people [68]. However, existing analysis tools take this into account, and ...…”