Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases

Bravata, Dena M; McDonald, Kathryn M; Smith, Wendy; Rydzak, Chara E.; Szeto, Herbert; Buckeridge, David L.; Haberland, Corinna; Owens, Douglas K

doi:10.7326/0003-4819-140-11-200406010-00013

Cited by 245 publications

(162 citation statements)

References 97 publications

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“…During the past decade, increased awareness of the need to recognize the introduction of pathogens in a monitored population as early as possible has caused a shift in disease surveillance towards systems that can provide timely detection [1,2]. Some monitoring has shifted to pre-diagnostic data, which are available early, but lack specificity for the detection of particular diseases.…”

Section: Introductionmentioning

confidence: 99%

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Dórea

McEwen

McNab

et al. 2013

J. R. Soc. Interface.

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Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

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Section: Introductionmentioning

confidence: 99%

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Dórea

McEwen

McNab

et al. 2013

J. R. Soc. Interface.

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“…on real-time data from the Department of Defense Military Health System [6] and BioSense, based on data from the Department of Veterans Affairs, the Department of Defense, retail pharmacies, and Laboratory Corporation of America [7]. These systems are designed to facilitate early detection of disease outbreaks as well as response to harmful health effects, exposure to disease, or related hazardous conditions.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…Situational awareness is the real-time analysis and display of health data to monitor the location, magnitude, and spread of an outbreak. As Bravata et al (2004) said, "...an essential component of preparations for illnesses and syndromes potentially related to bioterrorism includes the deployment of surveillance systems that can rapidly detect and monitor [emphasis added] the course of an outbreak and thus minimize associated morbidity and mortality. "…”

Section: Introductionmentioning

confidence: 99%

“…Sosin (2005) states that approximately 100 state and local health jurisdictions were conducting some form of syndromic surveillance in 2003, Bravata et al (2004 conducted a systematic review of the publicly available literature and various websites from which they identified 115 biosurveillance systems.…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Methodology for Real-Time Biosurveillance

Fricker

Chang

2008

Quality Engineering

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In this paper we introduce a new spatio-temporal methodology for biosurveillance entitled the Repeated Two-sample Rank (RTR) procedure. It is designed to sequentially incorporate information from individual observations and thus can operate on data in real-time as it arrives into an automated biosurveillance system. In addition, upon a signal of a possible outbreak, the methodology suggests a way to graphically indicate the likely outbreak location, and the output can subsequently be used to track the spread of an outbreak. Thus, the methodology can be used for both early event detection and situational awareness in automated biosurveillance systems.

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Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases

Cited by 245 publications

References 97 publications

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Global Disease Monitoring and Forecasting with Wikipedia

A Spatio-Temporal Methodology for Real-Time Biosurveillance

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