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PLoS ONE, 13 (10), ISSN:1932-6203

Digital epidemiology, also referred to as digital disease detection (DDD), is motivated by the same objectives as traditional epidemiology. However, DDD focuses on electronic data sources that emerged with the advent of information technology [1–3]. It draws on developments such as the widespread availability of Internet access, the explosive growth in mobile devices, and online sharing platforms, which constantly generate vast amounts of data containing health-related information, even though they are not always collected with public health as an objective. Furthermore, this novel approach builds on the idea that information relevant to public health is now increasingly generated directly by the population through their use of online services, without their necessarily having engaged with the health care system [4, 5]. By utilizing global real-time data, DDD promises accelerated disease outbreak detection, and examples of this enhanced timeliness in detection have already been reported in the literature. The most recent example is the 2014 Ebola virus outbreak in West Africa [6]. Reports of the emerging outbreak were detected by digital surveillance channels in advance of official reports. Furthermore, information gleaned by the various datasets can be used for several epidemiological purposes beyond early detection of disease outbreaks [7, 8], such as the assessment of health behavior and attitudes [4] and pharmacovigilance [9].

Note: Editorial Reference EPFL-ARTICLE-214482doi:10.1371/journal.pcbi.1003904 Record created on 2015-12-10, modified on 2017-05-12