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Related Experiment Video

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Respiratory syncytial virus tracking using internet search engine data.

Eyal Oren1,2, Justin Frere3, Eran Yom-Tov4

  • 1Division of Epidemiology & Biostatistics, Graduate School of Public Health, San Diego State University, San Diego, CA, USA. eoren@sdsu.edu.

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|April 5, 2018
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Summary
This summary is machine-generated.

Internet search data can effectively track Respiratory Syncytial Virus (RSV) spread across the US. This method offers a novel way to monitor seasonal disease activity, especially when traditional data is limited.

Keywords:
Domain adaptationGoogle trendsInternet dataRSV

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Area of Science:

  • Epidemiology
  • Public Health
  • Digital Health

Background:

  • Respiratory Syncytial Virus (RSV) is a primary cause of hospitalization for infants in the US.
  • Internet search queries offer high-resolution data for disease estimation and prediction.

Purpose of the Study:

  • To build predictive models for RSV incidence using internet search data.
  • To track the spatiotemporal spread of RSV across the United States.

Main Methods:

  • Filtered 613 symptoms from Bing search logs, then used Google Trends data (2004-2016) for 50 terms.
  • Developed predictive models for RSV incidence in five states with surveillance data.
  • Applied domain adaptation to model RSV incidence in the remaining 45 states.

Main Results:

  • Surveillance data (hospitalizations, lab reports) correlated highly with each other and with search engine data.
  • RSV, flu, pneumonia, and bronchiolitis search terms showed significant correlation with surveillance data.
  • Modeled RSV spread showed a general progression from the southeastern to the northwestern US.

Conclusions:

  • This study is the first to track RSV using internet search data.
  • Search filtering and domain adaptation techniques proved effective for analyzing multi-resolution data.
  • The approach can aid in identifying local and widespread RSV transmission and is applicable to other seasonal diseases.