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Using Search Engine Data as a Tool to Predict Syphilis.

Sean D Young1,2, Elizabeth A Torrone3, John Urata2

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Internet search data can predict syphilis outbreaks. Google Trends data accurately forecasted weekly syphilis cases, suggesting its potential for public health surveillance of sexually transmitted diseases.

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Digital Epidemiology

Background:

  • Sexually transmitted diseases (STDs), including syphilis, pose a significant public health challenge.
  • Individuals at risk for STDs may utilize online resources to seek health information.
  • Previous research suggests the potential of social media and online search data for disease monitoring.

Purpose of the Study:

  • To investigate the association between internet search query data and reported weekly syphilis cases.
  • To assess the feasibility of using online search trends for predicting syphilis incidence.

Main Methods:

  • Utilized weekly reported primary and secondary syphilis case counts (2012-2014) from the US Centers for Disease Control and Prevention.
  • Collected weekly internet search query data for 25 risk-related keywords from Google Trends (2012-2014).
  • Developed and validated linear mixed-effects models using Google Trends data with a 1-week lag to predict syphilis cases.

Main Results:

  • Models accurately predicted syphilis counts for 144 weeks across all states.
  • Achieved an overall average R-squared value of 0.9, indicating strong predictive power.
  • Demonstrated an overall average root mean squared error of 4.9, signifying high accuracy.

Conclusions:

  • Google Trends search data from the preceding week can effectively predict future syphilis cases at the state level.
  • Online search data shows promise for integration into public health surveillance systems for STDs.
  • Further research is warranted to explore the full potential of digital data in STD monitoring and prevention efforts.