Abstract
The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
Highlights
Increases in data volume, diversity and speed have affected all aspects of human life
While health surveillance systems continue to adapt, improving traditional components [e.g., [2,3,4]] and adding others based on the exploitation of novel data streams [e.g., [5,6,7,8]], their progress fades in comparison to that seen in other sectors [1], from business and marketing to the more related area of diagnostic services within human health
Informed by a literature search targeting articles in the health surveillance domain which used the term “big data,” workshop discussions were organized into four main groups of “big data analytics” (BDA) challenges: technical, operational, normative, and funding
Summary
Diversity and speed have affected all aspects of human life. As we advance into the 21st century, Simonsen et al [1] highlight two main streams that are pushing health surveillance into the “Big Data Era”: the advancements in laboratorial detection tools which traditional surveillance rely on, and a dramatic increase in the number of health and non-health related data streams that can be exploited for surveillance. While health surveillance systems continue to adapt, improving traditional components [e.g., [2,3,4]] and adding others based on the exploitation of novel data streams [e.g., [5,6,7,8]], their progress fades in comparison to that seen in other sectors [1], from business and marketing to the more related area of diagnostic services within human health. Discussions around the exploitation of novel data streams has been focused almost exclusively on emergence prediction and early disease detection, in detriment of other surveillance goals, such as situational awareness for non-communicable and endemic diseases, and disease freedom demonstration
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