Converting Big Data into public health.
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We concur with M. J. Khoury and J. P. A. Ioannidis (“Big data meets public health,” Perspectives, 28 November 2014, p. [1054][1]) that “a strong epidemiologic foundation, robust knowledge integration, principles of evidence-based medicine, and an expanded translation research agenda” can facilitate public health applications of Big Data. However, Big Data scientists and public health practitioners must forge better relationships if this vision is to be realized.
Big Data analysis algorithms should be accessible to health practitioners ([ 1 ][2]). This requires more than freely accessible computer code and a user-friendly Graphical User Interface. Outreach programs, self-learning modules, and textbooks that are designed for users without a programming background will help facilitate the integration of these algorithms and statistical tools into public health, medical, nursing, and other health care–related degree programs, graduates of which are the bulk of the domestic and global public health workforce. The average public health practitioner is not a computer scientist.
The Ebola Response model from the Centers for Disease Control and Prevention (CDC) ([ 2 ][3]) provides a good example in the field of infectious disease modeling. The Modeling Task Force of CDC's Ebola Emergency Response fits epidemiological data to a spreadsheet-based Markov chain model to provide policy-makers with incidence projections, with and without scale-up of effective interventions. Using it as a communication tool, the task force communicated the possible dire consequences of the Ebola outbreak to senior decision-makers. This simple model had a substantial policy impact on the global Ebola response because of its ability to convey a powerful message to policy-makers and facilitate resource mobilization and prompt action. Ultimately, models are tools of communication to policy-makers. Consultation with the intended audience helps ensure that the product meets their needs. Likewise, Big Data analyses should help practitioners inform policy-makers about policy options and how they should craft their messages to the general public ([ 3 ][4]).
1. [↵][5] 1. D. Lazer, 2. R. Kennedy, 3. G. King, 4. A. Vespignani
, Science 343, 1203 (2014).
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2. [↵][8] 1. M. I. Meltzer 2. et al
., MMWR Surveill. Summ. 63 (suppl. 3), 1 (2014).
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3. [↵][11] 1. A. B. Heldman, 2. J. Schindelar, 3. J. B. Weaver III
, Public Health Rev. 35, 1 (2013); [www.publichealthreviews.eu/show/a/129][12].
[OpenUrl][13][Web of Science][14]
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[1] James B. Weaver,et al. Social Media Engagement and Public Health Communication: Implications for Public Health Organizations Being Truly “Social” , 2013, Public Health Reviews.
[2] D. Lazer,et al. The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.