A Heuristic Indication and Warning Staging Model for Detection and Assessment of Biological Events

Wilson, James M.; Polyak, Marat G.; Blake, Jane; Collmann, Jeff

doi:10.1197/jamia.m2558

Cited by 32 publications

(24 citation statements)

References 8 publications

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“…[6-9] Argus collects, in an automated process, several thousand local, native-language Internet media articles daily. [10] Bayesian software tools and Boolean search strings, based on a taxonomy of infectious disease, identify candidate relevant articles.…”

Section: Methodsmentioning

confidence: 99%

Event-based internet biosurveillance: relation to epidemiological observation

Nelson

Reilly

et al. 2012

Emerg Themes Epidemiol

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BackgroundThe World Health Organization (WHO) collects and publishes surveillance data and statistics for select diseases, but traditional methods of gathering such data are time and labor intensive. Event-based biosurveillance, which utilizes a variety of Internet sources, complements traditional surveillance. In this study we assess the reliability of Internet biosurveillance and evaluate disease-specific alert criteria against epidemiological data.MethodsWe reviewed and compared WHO epidemiological data and Argus biosurveillance system data for pandemic (H1N1) 2009 (April 2009 – January 2010) from 8 regions and 122 countries to: identify reliable alert criteria among 15 Argus-defined categories; determine the degree of data correlation for disease progression; and assess timeliness of Internet information.ResultsArgus generated a total of 1,580 unique alerts; 5 alert categories generated statistically significant (p < 0.05) correlations with WHO case count data; the sum of these 5 categories was highly correlated with WHO case data (r = 0.81, p < 0.0001), with expected differences observed among the 8 regions. Argus reported first confirmed cases on the same day as WHO for 21 of the first 64 countries reporting cases, and 1 to 16 days (average 1.5 days) ahead of WHO for 42 of those countries.ConclusionConfirmed pandemic (H1N1) 2009 cases collected by Argus and WHO methods returned consistent results and confirmed the reliability and timeliness of Internet information. Disease-specific alert criteria provide situational awareness and may serve as proxy indicators to event progression and escalation in lieu of traditional surveillance data; alerts may identify early-warning indicators to another pandemic, preparing the public health community for disease events.

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Section: Methodsmentioning

confidence: 99%

Event-based internet biosurveillance: relation to epidemiological observation

Nelson

Reilly

et al. 2012

Emerg Themes Epidemiol

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“…Project Argus is another Internet-based biosurveillance project hosted at the Georgetown University Medical Center. A retrospective study of the SARS outbreak by Georgetown researchers showed that subtle indications (unseasonal bad influenza) were reported in online news sources as early as September 2002 [10]. Despite the great potential in mining early indications of disease outbreaks from online sources, identification of relevant online articles is challenging in practice due to the vast amount of ever growing publications on the Internet.…”

Section: Introductionmentioning

confidence: 99%

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

Torii

Yin

Nguyễn

et al. 2011

International Journal of Medical Informatics

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Purpose Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages.

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“…14,15 Open-source media analysis may detect evidence of biological threats and/or social disruption, leading to direct and indirect indicators of outbreaks. 16 Moreover, this analysis may provide warning of outbreaks sooner than traditional surveillance methods. 17 Sophisticated analytic techniques have been applied to existing data sources to better distinguish anomalies from baseline data.…”

Section: Event-based Biosurveillancementioning

confidence: 99%

“…Direct indicators and warnings are unambiguous information that an event is indeed occurring. 16 Indirect indicators and warnings are proxy data that indicate the circumstances wherein a biosurveillance event is likely to occur.…”

Section: Input Data Attribute Familymentioning

confidence: 99%

Assessing the Continuum of Event-Based Biosurveillance Through an Operational Lens

Corley

Lancaster

Brigantic

et al. 2012

Biosecurity and Bioterrorism: Biodefense Strategy, Practice, an

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This research follows the Updated Guidelines for Evaluating Public Health Surveillance Systems, Recommendations from the Guidelines Working Group, published by the Centers for Disease Control and Prevention nearly a decade ago. Since then, models have been developed and complex systems have evolved with a breadth of disparate data to detect or forecast chemical, biological, and radiological events that have a significant impact on the One Health landscape. How the attributes identified in 2001 relate to the new range of event-based biosurveillance technologies is unclear. This article frames the continuum of event-based biosurveillance systems (that fuse media reports from the internet), models (ie, computational that forecast disease occurrence), and constructs (ie, descriptive analytical reports) through an operational lens (ie, aspects and attributes associated with operational considerations in the development, testing, and validation of the event-based biosurveillance methods and models and their use in an operational environment). A workshop was held in 2010 to scientifically identify, develop, and vet a set of attributes for event-based biosurveillance. Subject matter experts were invited from 7 federal government agencies and 6 different academic institutions pursuing research in biosurveillance event detection. We describe 8 attribute families for the characterization of event-based biosurveillance: event, readiness, operational aspects, geographic coverage, population coverage, input data, output, and cost. Ultimately, the analyses provide a framework from which the broad scope, complexity, and relevant issues germane to event-based biosurveillance useful in an operational environment can be characterized.

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A Heuristic Indication and Warning Staging Model for Detection and Assessment of Biological Events

Abstract: Used as a complement to current epidemiological surveillance methods, our approach could aid global public health officials and national political leaders in responding to biological threats of international public health significance.

Cited by 32 publications

References 8 publications

Event-based internet biosurveillance: relation to epidemiological observation

Event-based internet biosurveillance: relation to epidemiological observation

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

Assessing the Continuum of Event-Based Biosurveillance Through an Operational Lens

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