Estimating disease burden using Internet data

Riyi Qiu, Mirsad Hadzikadic, Sha Yu, Lixia Yao

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases. We found that Google search volume is relatively strongly correlated with the burdens for 39 of 1633 diseases, including viral hepatitis, diabetes mellitus, multiple sclerosis, and hemorrhoids. Wikipedia and Twitter data strongly correlated with the burdens of 15 and 7 diseases, respectively. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and the presence of stigma.

Original languageEnglish (US)
JournalHealth Informatics Journal
DOIs
StateAccepted/In press - Jan 1 2018

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Keywords

  • data mining
  • disease burden
  • Google search
  • least absolute shrinkage and selection operator
  • prevalence
  • treatment cost
  • Twitter
  • Wikipedia

ASJC Scopus subject areas

  • Health Informatics

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