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

Ning Li; Guoqi Qian; Richard Huggins

doi:10.1016/j.csda.2005.06.011

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

Ning Li, Guoqi Qian, Richard Huggins

Research output: Contribution to journal › Article › Research › peer-review

1 Citation (Scopus)

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	https://doi.org/10.1016/j.csda.2005.06.011
Publication status	Published - 20 Jul 2006
Externally published	Yes

Keywords

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

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10.1016/j.csda.2005.06.011

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Cite this

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title = "A latent variable model for estimating disease transmission rate from data on household outbreaks",

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.",

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AU - Li, Ning

AU - Qian, Guoqi

AU - Huggins, Richard

PY - 2006/7/20

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N2 - 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.

AB - 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.

KW - Bayesian statistics

KW - Data augmentation

KW - Disease transmission rate

KW - Infection chain

KW - Outbreak size

KW - Tanner-Wong algorithm

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