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 language | English |
|---|---|
| Pages (from-to) | 3354-3368 |
| Number of pages | 15 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 50 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 20 Jul 2006 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Bayesian statistics
- Data augmentation
- Disease transmission rate
- Infection chain
- Outbreak size
- Tanner-Wong algorithm
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