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


Höhle, M; Paul, M; Held, L (2009). Statistical approaches to the monitoring and surveillance of infectious diseases for veterinary public health. Preventive Veterinary Medicine, 91(1):2-10.

Abstract

This paper covers the aspect of using statistical methodology for the monitoring and surveillance of routinely collected data in veterinary public health. An account of the Farrington algorithm and Poisson cumulative sum schemes for the prospective detection of aberrations is given with special attention devoted to the occurrence of seasonality and spatial aggregation of the time series. Modelling approaches for retrospective analysis of surveillance counts are also described. To illustrate the applicability of the methodology in veterinary public health, data from the monitoring of rabies among fox in Hesse, Germany, are analysed.

Abstract

This paper covers the aspect of using statistical methodology for the monitoring and surveillance of routinely collected data in veterinary public health. An account of the Farrington algorithm and Poisson cumulative sum schemes for the prospective detection of aberrations is given with special attention devoted to the occurrence of seasonality and spatial aggregation of the time series. Modelling approaches for retrospective analysis of surveillance counts are also described. To illustrate the applicability of the methodology in veterinary public health, data from the monitoring of rabies among fox in Hesse, Germany, are analysed.

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18 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2009
Deposited On:01 Dec 2009 13:37
Last Modified:05 Apr 2016 13:35
Publisher:Elsevier
ISSN:0167-5877
Publisher DOI:https://doi.org/10.1016/j.prevetmed.2009.05.017
PubMed ID:19539387

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