Modelling and inference for epidemic models featuring non-linear infection pressure

P. D. O'Neill; C. H. Wen

doi:10.1016/j.mbs.2012.03.007

Modelling and inference for epidemic models featuring non-linear infection pressure

P. D. O'Neill^*, C. H. Wen

^*Corresponding author for this work

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

9 Citations (Scopus)

Abstract

We consider a Susceptible-Infective-Removed (SIR) stochastic epidemic model in which the infection rate is of the form βN ^-1X(t)Y(t) ^α. It is demonstrated that both the threshold behaviour of this model and the behaviour of the corresponding deterministic model differ markedly from the standard SIR model (i.e. α=1). Methods of statistical inference for this model are described, given outbreak data, and the extent to which all three model parameters can be estimated is considered.

Original language	English
Pages (from-to)	38-48
Number of pages	11
Journal	Mathematical Biosciences
Volume	238
Issue number	1
DOIs	https://doi.org/10.1016/j.mbs.2012.03.007
Publication status	Published - Jul 2012
Externally published	Yes

Keywords

Bayesian inference
Epidemic model
Markov chain Monte Carlo methods
SIR model

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10.1016/j.mbs.2012.03.007

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abstract = "We consider a Susceptible-Infective-Removed (SIR) stochastic epidemic model in which the infection rate is of the form βN -1X(t)Y(t) α. It is demonstrated that both the threshold behaviour of this model and the behaviour of the corresponding deterministic model differ markedly from the standard SIR model (i.e. α=1). Methods of statistical inference for this model are described, given outbreak data, and the extent to which all three model parameters can be estimated is considered.",

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AB - We consider a Susceptible-Infective-Removed (SIR) stochastic epidemic model in which the infection rate is of the form βN -1X(t)Y(t) α. It is demonstrated that both the threshold behaviour of this model and the behaviour of the corresponding deterministic model differ markedly from the standard SIR model (i.e. α=1). Methods of statistical inference for this model are described, given outbreak data, and the extent to which all three model parameters can be estimated is considered.

KW - Bayesian inference

KW - Epidemic model

KW - Markov chain Monte Carlo methods

KW - SIR model

UR - https://www.scopus.com/pages/publications/84861570908

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DO - 10.1016/j.mbs.2012.03.007

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VL - 238

SP - 38

EP - 48

JO - Mathematical Biosciences

JF - Mathematical Biosciences

IS - 1

ER -

Modelling and inference for epidemic models featuring non-linear infection pressure

Abstract

Keywords

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