Evaluation and Extension of the Cusum Technique with an Application to <i>Salmonella</i> Surveillance

Carpenter, Tim E.

doi:10.1177/104063870201400304

Cited by 10 publications

(7 citation statements)

References 20 publications

(45 reference statements)

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“…Additionally, we did not use 0.2 when r xt was 0, rather the actual value. Based on the previous literatures [13,25,26], we determined the value range of H and k, as 3r 6 H 6 5r and 0 < k 6 1.5, respectively. The adjustment interval for k and H was set as 0.1 and 0.5r, respectively.…”

Section: Outbreak Detection Algorithmsmentioning

confidence: 99%

Comparing early outbreak detection algorithms based on their optimized parameter values

Wang

Zeng

Seale

et al. 2010

Journal of Biomedical Informatics

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Section: Outbreak Detection Algorithmsmentioning

confidence: 99%

Comparing early outbreak detection algorithms based on their optimized parameter values

Wang

Zeng

Seale

et al. 2010

Journal of Biomedical Informatics

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“…One alternative to relying on data shared voluntarily is the exploitation of automatically collected laboratory submission data (Stone, 2007). Laboratory test results have been analysed retrospectively to detect temporal clustering of bacterial pathogens in public health (Dessau and Steenberg, 1993;Hutwagner, et al, 1997;Widdowson, et al, 2003) and veterinary medicine (Carpenter, 2002;Zhang, et al, 2005). The use of submission data, however, more properly fits the purposes of syndromic surveillance, as test requests are available earlier, though provide less specificity, than test results.…”

Section: Introductionmentioning

confidence: 99%

Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance

Dórea

Revie

McEwen

et al. 2013

Preventive Veterinary Medicine

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The practice of disease surveillance has shifted in the last two decades towards the introduction of systems capable of early detection of disease. Modern biosurveillance systems explore different sources of pre-diagnostic data, such as patient's chief complaint upon emergency visit or laboratory test orders. These sources of data can provide more rapid detection than traditional surveillance based on case confirmation, but are less specific, and therefore their use poses challenges related to the presence of background noise and unlabelled temporal aberrations in historical data. The overall goal of this study was to carry out retrospective analysis using three years of laboratory test submissions to the Animal Health Laboratory in the province of Ontario, Canada, in order to prepare the data for use in syndromic surveillance. Daily cases were grouped into syndromes and counts for each syndrome were monitored on a daily basis when medians were higher than one case per day, and weekly otherwise. Poisson regression accounting for day-of-week and month was able to capture the day-of-week effect with minimal influence from temporal aberrations. Applying Poisson regression in an iterative manner, that removed data points above the predicted 95th percentile of daily counts, allowed for the removal of these aberrations in the absence of labelled outbreaks, while maintaining the day-of-week effect that was present in the original data. This resulted in the construction of time series that represent the baseline patterns over the past three years, free of temporal aberrations. The final method was thus able to remove temporal aberrations while keeping the original explainable effects in the data, did not need a training period free of aberrations, had minimal adjustment to the aberrations present in the raw data, and did not require labelled outbreaks. Moreover, it was readily applicable to the weekly data by substituting Poisson regression with moving 95th percentiles.

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“…Many different approaches have been applied [e.g. 2, [12][13][14][15][16] without consistency or apparent agreement on what is optimal. Evaluation of human health surveillance systems is more commonly practised and several generic guidelines exist for public health surveillance evaluation [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of animal and public health surveillance systems: a systematic review

et al. 2011

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Disease surveillance programmes ought to be evaluated regularly to ensure they provide valuable information in an efficient manner. Evaluation of human and animal health surveillance programmes around the world is currently not standardized and therefore inconsistent. The aim of this systematic review was to review surveillance system attributes and the methods used for their assessment, together with the strengths and weaknesses of existing frameworks for evaluating surveillance in animal health, public health and allied disciplines. Information from 99 articles describing the evaluation of 101 surveillance systems was examined. A wide range of approaches for assessing 23 different system attributes was identified although most evaluations addressed only one or two attributes and comprehensive evaluations were uncommon. Surveillance objectives were often not stated in the articles reviewed and so the reasons for choosing certain attributes for assessment were not always apparent. This has the potential to introduce misleading results in surveillance evaluation. Due to the wide range of system attributes that may be assessed, methods should be explored which collapse these down into a small number of grouped characteristics by focusing on the relationships between attributes and their links to the objectives of the surveillance system and the evaluation. A generic and comprehensive evaluation framework could then be developed consisting of a limited number of common attributes together with several sets of secondary attributes which could be selected depending on the disease or range of diseases under surveillance and the purpose of the surveillance. Economic evaluation should be an integral part of the surveillance evaluation process. This would provide a significant benefit to decision-makers who often need to make choices based on limited or diminishing resources.

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Evaluation and Extension of the Cusum Technique with an Application to Salmonella Surveillance

Cited by 10 publications

References 20 publications

Comparing early outbreak detection algorithms based on their optimized parameter values

Comparing early outbreak detection algorithms based on their optimized parameter values

Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance

Evaluation of animal and public health surveillance systems: a systematic review

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