A random effects model for diseases with heterogeneous rates of infection

Ning Li, Guoqi Qian, Richard Huggins

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4 Citations (Scopus)


One form of data collected in the study of infectious diseases is on the transmission of a disease within households. We consider a model which allows the rate of disease transmission to vary between households. A Bayesian hierarchical approach to fitting the model is proposed and is implemented by the Metropolis-Hastings algorithm, a standard Markov chain Monte Carlo (MCMC) method. Results are presented for both simulated epidemic chain data and the Providence measles data, illustrating the potential that MCMC methods have to dealing with heterogeneity in infectious disease transmission.

Original languageEnglish
Pages (from-to)317-332
Number of pages16
JournalJournal of Statistical Planning and Inference
Issue number1
Publication statusPublished - 1 Sep 2003
Externally publishedYes


  • Epidemic chain
  • Hierarchical model
  • Infectious disease
  • MCMC
  • Metropolis-Hastings algorithm
  • Parameter transformation
  • Random avoidance probability

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