Assessing the Impact of a Movement Network on the Spatiotemporal Spread of Infectious Diseases

Birgit Schrödle, Leonhard Held*, Haavard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated.

Original languageEnglish (US)
Pages (from-to)736-744
Number of pages9
JournalBiometrics
Volume68
Issue number3
DOIs
StatePublished - Sep 1 2012

Keywords

  • INLA
  • Infectious disease counts
  • Network data
  • Observation-driven
  • Parameter-driven
  • Spatiotemporal

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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