The Global Biosurveillance Portal: Biosurveillance for the Department of Defense

Anderson, Janelle J.; Rosenzweig, C. Nicole; Roos, Jason W.; Flores, Brandon

doi:10.5210/ojphi.v7i1.5729

Cited by 2 publications

(2 citation statements)

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“…The web-based information-sharing system GBSP 42 facilitates timely responses and decision-making to support the detection and management of natural and unnatural biological hazards. Based on the Ozone Widget Framework architecture, an open-source data integration framework, the GBSP provides end-users with a single web front-end to access reports in HTML frames from multiple web applications.…”

Section: Current Internet-based Epidemic Intelligence Systemsmentioning

confidence: 99%

Artificial intelligence in public health: the potential of epidemic early warning systems

et al. 2023

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The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to—not a replacement of—traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

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Section: Current Internet-based Epidemic Intelligence Systemsmentioning

confidence: 99%

Artificial intelligence in public health: the potential of epidemic early warning systems

et al. 2023

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“…12 It was designed and developed as an unclassified web application in a cloud environment using the Ozone Widget Framework (OWF) architecture. 13 In view of the perceived plausible threat of bioterrorist attacks in the Korean Peninsula, the ROK Ministry of National Defense (MND) and US Department of Defense (DoD) collaborated to form the first operational BSP late in 2014. 12 This implementation was intended to facilitate international collaboration, communication, and information sharing in support of the detection, management, and mitigation of man-made and naturally occurring biological events.…”

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confidence: 99%

Syndromic Surveillance System for Korea–US Joint Biosurveillance Portal: Design and Lessons Learned

Rhee¹,

Burkom²,

Yoon³

et al. 2016

Health Security

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Driven by the growing importance of situational awareness of bioterrorism threats, the Republic of Korea (ROK) and the United States have constructed a joint military capability, called the Biosurveillance Portal (BSP), to enhance biosecurity. As one component of the BSP, we developed the Military Active Real-time Syndromic Surveillance (MARSS) system to detect and track natural and deliberate disease outbreaks. This article describes the ROK military health data infrastructure and explains how syndromic data are derived and made available to epidemiologists. Queries corresponding to 8 syndromes, based on published clinical effects of weaponized pathogens, were used to classify military hospital patient records to form aggregated daily syndromic counts. A set of ICD-10 codes for each syndrome was defined through literature review and expert panel discussion. A study set of time series of national daily counts for each syndrome was extracted from the Defense Medical Statistical Information System between January 1, 2011, and May 31, 2014. A stratified, adjusted cumulative summation algorithm was implemented for each syndrome group to signal alerts prompting investigation. The algorithm was developed by calculating sensitivity to sets of 1,000 artificial outbreak signals randomly injected in the dataset, with each signal injected in a separate trial. Queries and visualizations were adapted from the Suite for Automated Global bioSurveillance. Findings indicated that early warning of outbreaks affecting fewer than 50 patients will require analysis at subnational levels, especially for common syndrome groups. Developing MARSS to improve sensitivity will require modification of underlying syndromic diagnosis codes, engineering to coordinate alerts among subdivisions, and enhanced algorithms. The bioterrorist threat in the Korean peninsula mandates these efforts.

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The Global Biosurveillance Portal: Biosurveillance for the Department of Defense

Cited by 2 publications

References 0 publications

Artificial intelligence in public health: the potential of epidemic early warning systems

Artificial intelligence in public health: the potential of epidemic early warning systems

Syndromic Surveillance System for Korea–US Joint Biosurveillance Portal: Design and Lessons Learned

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