TY - JOUR

T1 - Infectious Disease Surveillance in the Big Data Era

T2 - Towards Faster and Locally Relevant Systems

AU - Simonsen, Lone

AU - Gog, Julia R.

AU - Olson, Don

AU - Viboud, Cecile

PY - 2016/12/1

Y1 - 2016/12/1

N2 - While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.

AB - While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.

KW - big data

KW - medical claims

KW - Internet search queries

KW - syndromic data

KW - death certificates

KW - electronic patient records

KW - influenza

KW - infectious diseases surveillance

KW - real-time monitoring

U2 - 10.1093/infdis/jiw376

DO - 10.1093/infdis/jiw376

M3 - Journal article

C2 - 28830112

VL - 214

SP - S380-S385

JO - Journal of Infectious Diseases

JF - Journal of Infectious Diseases

SN - 0022-1899

IS - Supplement 4

ER -