Digital Epidemiology

Abstract

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges.

Crisis analytics: big data-driven crisis response

“…The big crisis data analytics, broadly speaking, aims to leverage big data analytics techniques, along with digital platforms (such as mobile phones/Internet), for efficient humanitarian response to different crises. There are many thematic applications of big crisis data including (1) data-driven digital epidemiology (in which public health research is conducted using CDRs and social media) (Salathe et al 2012); (2) population surveillance and urban analytics (Boulos et al 2011) (in which big crisis data is used for tracking the movement of crisis-affected population during a crisis); (3) crisis informatics and sociology (Palen et al 2007) (in which data, along with participatory mapping and crowdsourcing technology, is used for analyzing the sociological behavior of the affected community through behavioral inference and "reality mining"-that is data mining to extract and study social patterns of an individual or a group of individuals).…”

Digital epidemiology reveals global childhood disease seasonality and the effects of immunization

“…Over the past decade, the Internet has become a significant health resource for the general public and health professionals (10,11). Internet query platforms, such as Google Trends, have provided powerful and accessible resources for identifying outbreaks and for implementing intervention strategies (12)(13)(14). Research on infectious disease informationseeking behavior has demonstrated that Internet queries can complement traditional surveillance by providing a rapid and efficient means of obtaining large epidemiological datasets (13,(15)(16)(17)(18).…”

“…Internet query platforms, such as Google Trends, have provided powerful and accessible resources for identifying outbreaks and for implementing intervention strategies (12)(13)(14). Research on infectious disease informationseeking behavior has demonstrated that Internet queries can complement traditional surveillance by providing a rapid and efficient means of obtaining large epidemiological datasets (13,(15)(16)(17)(18). For example, epidemiological information contained within Google Trends has been used in the study of rotavirus, norovirus, and influenza (14,15,17,18).…”

Ethics of the health-related internet of things: a narrative review

“…5 Invasive inferences can be made about users based on collected data, potentially resulting in discrimination or exclusion from data-driven services, or decision-making based upon private knowledge the user would otherwise not choose to share (Peppet 2014;Haddadi et al 2015;Kostkova et al 2016). For example, secondary effects of pharmaceuticals can be identified by comparing data from multiple clinical trials as well as 'informal sources', such as incidental self-reporting via social media and search engine queries (Salathé et al 2012). In this type of research, the connections that can be revealed by linking diverse datasets cannot be accurately predicted.…”