Comparison of Web-Based Biosecurity Intelligence Systems: BioCaster, EpiSPIDER and HealthMap

Lyon, Aidan; Nunn, Mike; Grossel, Geoff; Burgman, Mark A.

doi:10.1111/j.1865-1682.2011.01258.x

Cited by 58 publications

(62 citation statements)

References 8 publications

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“…The selected information was then presented in Google Maps mashups. However, these pioneer projects seem to have been discontinued, whilst the HealthMap platform is still active (see Lyon et al 2012 for a comparison of the three systems). 57 Big data produced by queries entered into search engines have also been utilised for public health surveillance projects.…”

Section: Digital Public Health Surveillancementioning

confidence: 99%

Big Data-Driven Health Surveillance

2018

The Big Data Agenda: Data Ethics and Critical Data Studies

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Section: Digital Public Health Surveillancementioning

confidence: 99%

Big Data-Driven Health Surveillance

2018

The Big Data Agenda: Data Ethics and Critical Data Studies

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“…These tools and techniques, sometimes referred to as systems of 'biosurveillance' (Lyon et al, 2012;Parry, 2012), monitor data from published news sources in order to locate infectious disease 'events' as they emerge Lyon et al, 2012). The global reach of these systems, combined with their location in countries of the global North (where expertise is deemed to be situated) has led to some criticism that they act predominantly in the interests of these specific countries (Weir and Mykhalovskiy, 2006;Bingham et al, 2008;Collier and Lakoff, 2008;Ingram, 2010b) These connections between government, power and space have been the subject of significant, Foucauldian-inspired, work in geography (Philo, 1992;Crampton and Elden, 2007;Ingram, 2010aIngram, , 2010b.…”

Section: Biosecurity and Biosurveillancementioning

confidence: 99%

(Re)locating the border: Pre-entry tuberculosis (TB) screening of migrants to the UK

Warren¹

2013

Geoforum

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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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Comparison of Web-Based Biosecurity Intelligence Systems: BioCaster, EpiSPIDER and HealthMap

Cited by 58 publications

References 8 publications

Big Data-Driven Health Surveillance

Big Data-Driven Health Surveillance

(Re)locating the border: Pre-entry tuberculosis (TB) screening of migrants to the UK

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

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