A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease

Farrington, C. P.; Andrews, Nick; Beale, Andrew D.; Catchpole, Mike

doi:10.2307/2983331

Cited by 346 publications

(410 citation statements)

References 16 publications

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“…Le Straat (2005) [11] comprehensively reviews to that date the statistical methods that have been applied for detecting or monitoring outbreaks and monitoring trends of diseases. Some of them have been implemented in the R-package surveillance, in particular, those by Stroup et al (1989) [24], Farrington et al (1996) [2] and Höhle and Riebler (2005) [10]. The list of contributions in surveillance is still growing (see, for instance, recent papers by Held et al (2005Held et al ( , 2006 [8,9]).…”

mentioning

confidence: 99%

Bayesian Markov switching models for the early detection of influenza epidemics

Martínez-Beneito

Conesa

López‐Quílez

et al. 2008

Statistics in Medicine

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The early detection of the outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white noise process depending on whether the system is in an epidemic or a nonepidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, the proposal provides the probability of being in an epidemic state at any given moment. The methodology is evaluated on influenza illness data obtained from the Sanitary Sentinel Network of the Comunitat Valenciana, one of the 17 autonomous regions in Spain.

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mentioning

confidence: 99%

Bayesian Markov switching models for the early detection of influenza epidemics

Martínez-Beneito

Conesa

López‐Quílez

et al. 2008

Statistics in Medicine

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“…For this reason, mathematical modeling techniques have been applied to population-based data. 116 Application to data captured for surveillance of CR-BSIs has not been comprehensively performed.…”

Section: Appropriate Statistical Methods For Analysis Must Be Appliedmentioning

confidence: 99%

Catheter‐related bloodstream infections in hematology

Worth

Slavin

Brown

et al. 2007

Cancer

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THE NEED FOR SURVEILLANCE AND LIMITATIONS OF CURRENT METHODSTunneled and nontunneled central venous catheters (CVCs) are necessary to facilitate treatment of many hematologic disorders, but catheter-related bloodstream infections (CR-BSIs) are an important cause of morbidity and mortality, and may lead to interruptions in planned therapy for malignancy or increases in length of hospital stay. In immunocompromised populations with neoplastic disease, risks for infection are not uniform, 1 and increased risk for bloodstream infections in patients with hematological malignancy may be related to the underlying malignancy, chemotherapy-induced granulocytopenia, breaches in skin or mucosal integrity, or invasive procedures. 2,3Standardized surveillance measures have not been used to assess interventions with potential impact on the epidemiology of CR-BSIs. For example, widespread use of antibiotic lock therapy,

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“…Among those, this section presents a set of representative algorithms, which are already in routine application at several public health institutions or which we think have the potential to become so. First we describe the Farrington method introduced by Farrington, Andrews, Beale, and Catchpole (1996) together with the improvements proposed by Noufaily, Enki, Farrington, Garthwaite, Andrews, and Charlett (2012). As a Bayesian counterpart to these methods we present the BODA method published by Manitz and Höhle (2013) which allows the easy integration of covariates.…”

Section: Using Surveillance In Selected Contextsmentioning

confidence: 99%

“…If the Anscombe residual of a count is higher than weightsThreshold it is reweighted accordingly in a second fitting of the GLM. Farrington et al (1996) used a value of 1 whereas Noufaily et al (2012) advise a value of 2.56 so that the reweighting procedure is less drastic, because it also shrinks the variance of the observations. The original method is widely used in public health surveillance (Hulth et al 2010).…”

Section: One Size Fits Them All For Count Datamentioning

confidence: 99%

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Monitoring Count Time Series inR: Aberration Detection in Public Health Surveillance

Salmon¹,

Schumacher²,

Höhle³

2016

J. Stat. Soft.

102

116

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Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihoodratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.

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A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease

Cited by 346 publications

References 16 publications

Bayesian Markov switching models for the early detection of influenza epidemics

Bayesian Markov switching models for the early detection of influenza epidemics

Catheter‐related bloodstream infections in hematology

Monitoring Count Time Series inR: Aberration Detection in Public Health Surveillance

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