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Statistical modeling of infectious disease surveillance data

Type of publication Not peer-reviewed
Publikationsform Contribution to book (non peer-reviewed)
Publication date 2013
Author Held Leonhard, Paul Michaela,
Project Multivariate analysis of dependent count data
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Contribution to book (non peer-reviewed)

Book Infectious Disease Surveillance
Editor , M’ikanatha N. M.
Publisher Wiley-Blackwell, Hoboken NJ
Page(s) 535 - 544
ISBN Print ISBN: 9780470654675, Online ISBN: 9781118543
Title of proceedings Infectious Disease Surveillance
DOI 10.1002/9781118543504.ch43


We review statistical models to analyze infectious disease surveillance data with particular emphasis on the analysis of time series of infectious disease counts. Motivated by surveillance data from Germany we outline several approaches to analyze a single time series and to predict subsequent trends in incidence. Appropriate methods for the comparison of statistical predictions are introduced and applied in order to decide which model gives better predictions. We then move on to the analysis of multiple time series with a focus on (1) identification of interdependencies between different time series representing incidence from different pathogens and (2) modeling the spatiotemporal spread of infectious disease incidence. Throughout the chapter we give references to other approaches for the analysis of infectious disease surveillance data.