A latent variable model for estimating disease transmission rate from data on household outbreaks

Ning Li, Guoqi Qian, Richard Huggins

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Abstract

A Bayesian latent variable model is proposed for studying household epidemics of infectious diseases in this paper. This model is more general and flexible than the commonly used chain binomial epidemic model. In particular, the model allows for the heterogeneity of the infection transmission rates in related to the sizes and generations of the infectives. Moreover, the model assumes the availability of only the household outbreak sizes, which is more reasonable than assuming the availability of the hardly observed infection chains. The Tanner-Wong's IP algorithm is employed for effective simulations and inferences of this model. Finally, this model was applied to analyzing a real data set on F-4 Asian influenza.

Original languageEnglish
Pages (from-to)3354-3368
Number of pages15
JournalComputational Statistics and Data Analysis
Volume50
Issue number11
DOIs
Publication statusPublished - 20 Jul 2006
Externally publishedYes

Keywords

  • Bayesian statistics
  • Data augmentation
  • Disease transmission rate
  • Infection chain
  • Outbreak size
  • Tanner-Wong algorithm

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