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Modeling seasonality in space-time infectious disease surveillance data


Held, Leonhard; Paul, Michaela (2012). Modeling seasonality in space-time infectious disease surveillance data. Biometrical journal. Biometrische Zeitschrift, 54(6):824-843.

Abstract

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one-step-ahead predictions if all three components allow for seasonal variation.

Abstract

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one-step-ahead predictions if all three components allow for seasonal variation.

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18 citations in Web of Science®
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:2012
Deposited On:07 Dec 2012 07:45
Last Modified:05 Apr 2016 16:10
Publisher:Wiley-VCH Verlag Berlin
Series Name:Biometrical journal. Biometrische Zeitschrift
ISSN:0323-3847
Publisher DOI:https://doi.org/10.1002/bimj.201200037
PubMed ID:23034894

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