Uncertainties in big data when using internet surveillance tools and social media for determining patterns in disease incidence

JAMA Ophthalmology

Volumn 135, Issue 4, 2017, Pages 402-

  Benke, Kurt K ^a  

^a The University of Melbourne   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CONJUNCTIVITIS; DISEASE SURVEILLANCE; ELECTRONIC MEDICAL RECORD; EPIDEMIC; ETHNICITY; EYE DISEASE; HUMAN; IMMIGRANT; INCIDENCE; INTERNET; LETTER; MORBIDITY; PRIORITY JOURNAL; SEARCH ENGINE; SOCIAL MEDIA; SOCIAL NORM; UNCERTAINTY; WEB BROWSER;

INCIDENCE; INTERNET; MORBIDITY; SOCIAL MEDIA; UNCERTAINTY;

EID: 85017635736     PISSN: 21686165     EISSN: None     Source Type: Journal    
DOI: 10.1001/jamaophthalmol.2017.0138     Document Type: Letter

References (5)

1. Deiner MS, Lietman TM, McLeod SD, Chodosh J, Porco TC. Surveillance tools emerging from search engines and social media data for determining eye disease patterns. JAMA Ophthalmol. 2016; 134(9): 1024-1030.
2. Sommer A. The utility of "big data" and social media for anticipating, preventing, and treating disease. JAMA Ophthalmol. 2016; 134(9): 1030-1031.
3. Azari AA, Barney NP. Conjunctivitis: a systematic review of diagnosis and treatment. JAMA. 2013; 310(16): 1721-1729.
4. Ernst and Young. Big data: changing the way businesses compete and operate http://www.ey.com/Publication/vwLUAssets/EY---Big-data:-changing-the-way-businesses-operate/$FILE/EY-Insights-on-GRC-Big-data.pdf. Accessed May 26, 2016.
5. Moss R, Zarebski A, Dawson P, McCaw JM. Forecasting influenza outbreak dynamics in Melbourne from internet search query surveillance data. Influenza Other Respir Viruses. 2016; 10(4): 314-323.