Using Google Trends to Predict Pediatric Respiratory Syncytial Virus Encounters at a Major Health Care System.

Journal Article (Journal Article)

To assess whether Google search activity predicts lead-time for pediatric respiratory syncytial virus (RSV) encounters within a major health care system. Internet user search and health system encounter database analysis. Pediatric RSV encounter volumes across all clinics and hospitals in the Duke Health system were tabulated from 2005 to 2016. North Carolina Google user search activity for RSV were obtained over the same time period. Time series analysis was used to compare RSV encounters and search activity. Cross-correlation was used to determine the 'lag' time difference between Google user search interest for RSV and observed Pediatric RSV encounter volumes. Google search activity and Pediatric RSV encounter volumes demonstrated strong seasonality with predilection for winter months. Granger Causality testing revealed that North Carolina RSV Google search activity can predict pediatric RSV encounters at our health system (F = 5.72, p < 0.0001). Using cross-correlation, increases in Google search activity provided lead time of 0.21 weeks (1.47 days) prior to observed increases in Pediatric RSV encounter volumes at our health system. RSV is a common cause of upper airway obstruction in pediatric patients for which pediatric otolaryngologists are consulted. We demonstrate that Google search activity can predict RSV patient interactions with a major health system with a measurable lead-time. The ability to predict when illnesses in a population result in increased health care utilization would be an asset to health system providers, planners and administrators. Prediction of RSV would allow specific care pathways to be developed and resource needs to be anticipated before actual presentation.

Full Text

Duke Authors

Cited Authors

  • Crowson, MG; Witsell, D; Eskander, A

Published Date

  • January 30, 2020

Published In

Volume / Issue

  • 44 / 3

Start / End Page

  • 57 -

PubMed ID

  • 31997013

Electronic International Standard Serial Number (EISSN)

  • 1573-689X

Digital Object Identifier (DOI)

  • 10.1007/s10916-020-1526-8

Language

  • eng

Conference Location

  • United States