Commentary
- 1 May 2015
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in Epidemiology
- Vol. 26 (3), 390-394
- https://doi.org/10.1097/ede.0000000000000274
Abstract
Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called “three V’s”: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.Keywords
This publication has 23 references indexed in Scilit:
- The influence of social networking sites on health behavior change: a systematic review and meta-analysisJournal of the American Medical Informatics Association, 2014
- Considerations for Oral Cholera Vaccine Use during Outbreak after Earthquake in Haiti, 2010−2011Emerging Infectious Diseases, 2011
- DAGittyEpidemiology, 2011
- Risk factors and interventions with statistically significant tiny effectsInternational Journal of Epidemiology, 2011
- The utility of “Google Trends” for epidemiological research: Lyme disease as an exampleGeospatial Health, 2010
- Google Trends: A Web‐Based Tool for Real‐Time Surveillance of Disease OutbreaksClinical Infectious Diseases, 2009
- Low P-Values or Narrow Confidence Intervals: Which Are More Durable?Epidemiology, 2001
- Data, Design, and Background Knowledge in Etiologic InferenceEpidemiology, 2001
- EPIDEMIOLOGY AND THE WEB OF CAUSATION - HAS ANYONE SEEN THE SPIDERSocial Science & Medicine, 1994
- BIAS DUE TO MISCLASSIFICATION IN THE ESTIMATION OF RELATIVE RISKAmerican Journal of Epidemiology, 1977