Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance

Keller, Mikaela; Blench, Michael; Tolentino, Herman D.; Freifeld, Clark C.; Mandl, Kenneth D.; Mawudeku, Abla; Eysenbach, Günther; Brownstein, John S.

doi:10.3201/eid1505.081114

Cited by 177 publications

(161 citation statements)

References 27 publications

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“…This web-based approach utilizes unstructured real-time or near real-time data to support and complement traditional indicator-based surveillance. Many internet biosurveillance systems have been developed with examples of well-established and analyzed systems including Argus, BioCaster, EpiSPIDER, GPHIN, HealthMap, MedISys, and ProMED-mail [3][4][5][6][7][8].…”

Section: Background Internet Biosurveillancementioning

confidence: 99%

KIWI: A technology for public health event monitoring and early warning detection

Mukhi

Andres²,

Demianyk³

et al. 2016

OJPHI

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Objectives: To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.Methods: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.Results: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).Discussion: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.Conclusions: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

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Section: Background Internet Biosurveillancementioning

confidence: 99%

KIWI: A technology for public health event monitoring and early warning detection

Mukhi

Andres²,

Demianyk³

et al. 2016

OJPHI

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“…Besides scientific publications, bio-surveillance systems aggregate and analyze news articles to identify health threats, such as disease outbreaks and food hazards. HealthMap (Freifeld et al, 2008) and EpiSpider (Keller et al, 2009) rely on user created ProMED reports and do not process documents automatically. Contrary to these userbased approaches, Global Health Monitor (Don et al, 2008) and the Medical Information System (MedISys) (Rortais et al, 2010) in combination with PULS (Steinberger et al, 2008) automatically extract entities and events from relevant medical news.…”

Section: Related Workmentioning

confidence: 99%

DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences

Erñst

Siu

Milchevski

et al. 2016

Proceedings of ACL-2016 System Demonstrations

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Despite the abundance of biomedical literature and health discussions in online communities, it is often tedious to retrieve informative contents for health-centric information needs. Users can query scholarly work in PubMed by keywords and MeSH terms, and resort to Google for everything else. This demo paper presents the DeepLife system, to overcome the limitations of existing search engines for life science and health topics. DeepLife integrates large knowledge bases and harnesses entity linking methods, to support search and exploration of scientific literature, newspaper feeds, and social media, in terms of keywords and phrases, biomedical entities, and taxonomic categories. It also provides functionality for entityaware text analytics over health-centric contents.

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“…Closer collaboration and improved communication between public health officials and primary health physicians, and the appointment of personnel dedicated to the notification and reporting of infectious diseases in addition to improved training of primary care physicians could advance infectious disease reporting and thus increase infectious disease surveillance. 2,3,4 [Braz J Infect Dis 2011;15 (2):188]©Elsevier Editora Ltda.…”

mentioning

confidence: 99%

Primary care physicians and infectious diseases' notification

Đurić¹,

Ilić²

2011

Braz J Infect Dis

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Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance

Cited by 177 publications

References 27 publications

KIWI: A technology for public health event monitoring and early warning detection

KIWI: A technology for public health event monitoring and early warning detection

DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences

Primary care physicians and infectious diseases' notification

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