Abstract
The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.
Highlights
The Morbidity and Mortality Weekly Reports of the U.S Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S, and the Project Tycho (PT) database provides an improved source of these weekly data
The six tested Google Trends (GT) models significantly tracked with the PT data for the overall U.S models
Below we present results for each geographic region’s AR(1) Akaike Information Criterion (AIC)(i*) model which allowed us to correct for autocorrelation and to explore the variability between the search terms included in each state’s best model
Summary
The Morbidity and Mortality Weekly Reports of the U.S Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S, and the Project Tycho (PT) database provides an improved source of these weekly data These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. Clinical criteria may lack specificity and sensitivity; and, while PCR test platforms can rapidly and accurately identify Bordetella in clinical specimens using bacterial DNA target sequences, they can sometimes generate false-negative and false-positive results[14] For these reasons, reports on disease incidence include some cases identified by culture, PCR, serology, or clinical diagnosis[15]. Google Trends data have been used to predict incidence of many infectious diseases, ranging from influenza to Lyme disease[19,20,21,22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
