36. Statistical modeling of infectious disease surveillance data

  1. Nkuchia M. M'ikanatha2,
  2. Ruth Lynfield3,
  3. Chris A. Van Beneden4 and
  4. Henriette de Valk5
  1. Leonhard Held and
  2. Michaela Paul

Published Online: 12 MAR 2013

DOI: 10.1002/9781118543504.ch43

Infectious Disease Surveillance, Second Edition

Infectious Disease Surveillance, Second Edition

How to Cite

Held, L. and Paul, M. (2013) Statistical modeling of infectious disease surveillance data, in Infectious Disease Surveillance, Second Edition (eds N. M. M'ikanatha, R. Lynfield, C. A. Van Beneden and H. de Valk), John Wiley & Sons Ltd, Oxford, UK. doi: 10.1002/9781118543504.ch43

Editor Information

  1. 2

    Division of Infectious Disease Epidemiology, Pennsylvania Department of Health, Harrisburg, PA, USA

  2. 3

    Minnesota Department of Health, St. Paul, MN, USA

  3. 4

    Respiratory Diseases Branch, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA

  4. 5

    Infectious Disease Department, Institut de Veille Sanitaire, Saint Maurice, France

Author Information

  1. Division of Biostatistics, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland

Publication History

  1. Published Online: 12 MAR 2013
  2. Published Print: 15 APR 2013

ISBN Information

Print ISBN: 9780470654675

Online ISBN: 9781118543504

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Keywords:

  • disease outbreaks;
  • epidemiologic methods;
  • population surveillance;
  • statistical models;
  • data analysis;
  • epidemiology, Germany

Summary

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.