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Title:

<EquationSource Format="TEX">$${\tt surveillance}$$</EquationSource> : An R package for the monitoring of infectious diseases

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Monitoring, Public health surveillance, Time series of counts, Outbreak detection, Univariate and multivariate surveillance

Monitoring, Public health surveillance, Time series of counts, Outbreak detection, Univariate and multivariate surveillance Minimize

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article

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Title:

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

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Public health surveillance aims at lessening disease burden, e.g., in case of infectious diseases by timely recognizing emerging outbreaks. 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 o...

Public health surveillance aims at lessening disease burden, e.g., in case of infectious diseases by timely recognizing emerging outbreaks. 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 functionality for the visualization, modelling and monitoring of surveillance time series. With respect to modelling we focus on univariate time series modelling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. This ranges from illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g, by spline-modelling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-Multinomial modelling. 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 likelihood-ratio 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. Minimize

Year of Publication:

2014-11-05

Document Type:

text

Subjects:

Statistics - Computation

Statistics - Computation Minimize

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310 Collections of general statistics *(computed)*

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Title:

Inference in disease transmission experiments by using stochastic epidemic models

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The paper extends the susceptible-exposed-infective-removed model to handle heterogeneity introduced by spatially arranged populations, biologically plausible distributional assumptions and incorporation of observations from additional diagnostic tests. These extensions are motivated by a desire to analyse disease transmission experiments in a m...

The paper extends the susceptible-exposed-infective-removed model to handle heterogeneity introduced by spatially arranged populations, biologically plausible distributional assumptions and incorporation of observations from additional diagnostic tests. These extensions are motivated by a desire to analyse disease transmission experiments in a more detailed fashion than before. Such experiments are performed by veterinarians to gain knowledge about the dynamics of an infectious disease. By fitting our spatial susceptible-exposed-infective-removed with diagnostic testing model to data for a specific disease and production environment a valuable decision support tool is obtained, e.g. when evaluating on-farm control measures. Partial observability of the epidemic process is an inherent problem when trying to estimate model parameters from experimental data. We therefore extend existing work on Markov chain Monte Carlo estimation in partially observable epidemics to the multitype epidemic set-up of our model. Throughout the paper, data from a Belgian classical swine fever virus transmission experiment are used as a motivating example. Copyright 2005 Royal Statistical Society. Minimize

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article

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Danish Informatics Network in the Agriculture Sciences The Royal Veterinary and Agricultural University

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to obtain the Ph.D. degree.

to obtain the Ph.D. degree. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.dina.dk/~hoehle/pubs/dina105.pdf

http://www.dina.dk/~hoehle/pubs/dina105.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Statistical approaches to the surveillance of infectious diseases for veterinary public health

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Statistical approaches to the surveillance of infectious diseases for veterinary public health

Statistical approaches to the surveillance of infectious diseases for veterinary public health Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://epub.ub.uni-muenchen.de/2093/1/techrep14.pdf

http://epub.ub.uni-muenchen.de/2093/1/techrep14.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Aberration detection in R illustrated by Danish mortality monitoring

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The present text is a preprint of a book chapter to appear in T. Kass-Hout and X. Zhang (Eds.) Biosurveillance: A Health Protection Priority, CRC Press. 1.

The present text is a preprint of a book chapter to appear in T. Kass-Hout and X. Zhang (Eds.) Biosurveillance: A Health Protection Priority, CRC Press. 1. Minimize

Publisher:

CRC Press

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-13

Source:

http://www.statistik.lmu.de/~hoehle/pubs/hoehle_mazick2009-preprint.pdf

http://www.statistik.lmu.de/~hoehle/pubs/hoehle_mazick2009-preprint.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

The function ’algo.glrnb ’ in the R-Package ’surveillance’

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The aim of this document is to show the use of the function algo.glrnb for a type of count data regression chart, the generalized likelihood ratio (GLR) statistic. The function is part of the R-Package ’surveillance’ (Höhle, 2007), which provides outbreak detection algorithms for surveillance data. For an introduction to this package, the vignet...

The aim of this document is to show the use of the function algo.glrnb for a type of count data regression chart, the generalized likelihood ratio (GLR) statistic. The function is part of the R-Package ’surveillance’ (Höhle, 2007), which provides outbreak detection algorithms for surveillance data. For an introduction to this package, the vignette for the package can be used (Höhle et al., 2007). There one can find information about the data structure of the disProg and SurvRes objects. Furthermore tools for outbreak detection, such as a Bayesian approach, procedures described by Stroup et al. (1989), Farrington et al. (1996) and the methods used at the Robert Koch Institut, Germany, are explained. The function algo.glrnb is the implementation of the control charts for poisson and negative binomial distributions for monitoring time series of counts described in Höhle and Paul (2008). This document gives an overview of the different features of the function and illustrations of its use are given for simulated and real surveillance data. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-01-18

Source:

http://cran.r-project.org/web/packages/surveillance/vignettes/glrnb.pdf

http://cran.r-project.org/web/packages/surveillance/vignettes/glrnb.pdf Minimize

Document Type:

text

Language:

en

Subjects:

change-point detection ; generalized regression charts ; poisson

change-point detection ; generalized regression charts ; poisson Minimize

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310 Collections of general statistics *(computed)*

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Title:

Bayesian Estimation of the Size of a Population

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We consider the following problem: estimate the size of a population marked with serial numbers after only a sample of the serial numbers has been ob-served. Its simplicity in formulation and the inviting possibilities of applica-tion make this estimation well suited for an undergraduate level probability course. Our contribution consists in a B...

We consider the following problem: estimate the size of a population marked with serial numbers after only a sample of the serial numbers has been ob-served. Its simplicity in formulation and the inviting possibilities of applica-tion make this estimation well suited for an undergraduate level probability course. Our contribution consists in a Bayesian treatment of the problem. For an improper uniform prior distribution, we show that the posterior mean and variance have nice closed form expressions and we demonstrate how to compute highest posterior density intervals. Maple and R code is provided on the authors ’ web-page to allow students to verify the theoretical results and experiment with data. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-14

Source:

http://epub.ub.uni-muenchen.de/2094/1/paper_499.pdf

http://epub.ub.uni-muenchen.de/2094/1/paper_499.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Bayesian inference ; Combinatorics ; Hypergeometric functions ; Maple ; R

Bayesian inference ; Combinatorics ; Hypergeometric functions ; Maple ; R Minimize

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Title:

Decision Making based on Sampled Disease Occurrence in Animal Herds

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Abstract. To make qualified decisions when extrapolating results from a survey sample with imprecise tests requires careful handling of uncertainty. Both the imprecise test and uncertainty introduced by the sampling have to be taken into account in order to act optimally. This paper formulates an influence diagram with discrete and continuous no...

Abstract. To make qualified decisions when extrapolating results from a survey sample with imprecise tests requires careful handling of uncertainty. Both the imprecise test and uncertainty introduced by the sampling have to be taken into account in order to act optimally. This paper formulates an influence diagram with discrete and continuous nodes to handle an example typical for animal production: a veterinarian who – as part of a biosecurity program – has to decide whether to treat a herd of animals after inspecting a small fraction of them. Our aim is to investigate the robustness of the obtained strategy by performing a two-way sensitivity analysis with respect to the proportion of false positives and false negatives of the test. Output of the analysis is a treatment map illustrating how the chosen strategy varies according to variation in these proportions. The map helps to investigate whether a certain variation is acceptable or if the test procedure has to be standardized in order to reduce variation. Objective of the paper is to be an appetizer to work more with the issues raised in obtaining a practical solution. 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.stat.uni-muenchen.de/~hoehle/pubs/ecsqaru2003.pdf

http://www.stat.uni-muenchen.de/~hoehle/pubs/ecsqaru2003.pdf Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Title:

Poisson

Description:

regression charts for the monitoring of surveillance time series

regression charts for the monitoring of surveillance time series Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-08-14

Source:

http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper500.pdf

http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper500.pdf Minimize

Document Type:

text

Language:

en

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