Automated real time constant-specificity surveillance for disease outbreaks

Wieland, Shannon C.; Brownstein, John S.; Berger, Bonnie; Mandl, Kenneth D.

doi:10.1186/1472-6947-7-15

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Cusums applied to public health surveillance data which have high variance have been associated with a greater than expected number of false alarms in comparison to data with low variance [ 10 ]. Similarly, public health surveillance data with fluctuating variance has also been associated with variable specificity of surveillance algorithms [ 11 ]. Although a negative binomial cusum may provide a means to moderate the reported high false alarm rates associated with the use of established cusum based on other statistical models, the performance of negative binomial cusums has not been widely investigated.…”

Section: Introductionmentioning

confidence: 99%

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Watkins

Eagleson

Veenendaal

et al. 2008

BMC Med Inform Decis Mak

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Background: The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.

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Section: Introductionmentioning

confidence: 99%

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Watkins

Eagleson

Veenendaal

et al. 2008

BMC Med Inform Decis Mak

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“…Hierarchical generalized linear mixed models [32] are another methodology for fitting non-normal correlated data. Another approach of semiparametric regression could be considered; these combine parametric models for representing series data and non-parametric models for including possible trends and seasonal effects, such as smoothing methods [33] and generalized additive models [34]. Although these regression methods take into account trends and seasonal components, the major disadvantage of using these techniques for fallen stock data is that the models did not consider the temporal correlation structure.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits

et al. 2020

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Background: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. Results: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun-2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. Conclusions: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

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“…Wieland et al. (2007) proposed to model both the mean μ t and the variance at each time point t , using separate generalized additive models (GAMs) for both quantities.…”

Section: Regression Techniquesmentioning

confidence: 99%

“…This approach was proposed by Zhang et al. (2003), whose simple and hence easily automated wavelet‐based anomaly detector subtracts a baseline value obtained by using the wavelet transform and bases thresholds on the distribution of the residuals (see also Wieland et al. (2007)).…”

Section: Regression Techniquesmentioning

confidence: 99%

Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A Review

Unkel

Farrington

Garthwaite

et al. 2011

Journal of the Royal Statistical Society Series A: Statistics in Society

286

257

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Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks. This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods. This paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods.

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Automated real time constant-specificity surveillance for disease outbreaks

Cited by 19 publications

References 28 publications

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits

Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A Review

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