Multi-species SIR models from a dynamical Bayesian perspective

Lili Zhuang; Noel Cressie; Laura Pomeroy; Daniel Janies

doi:10.1007/s12080-013-0180-x

Multi-species SIR models from a dynamical Bayesian perspective

Lili Zhuang^*, Noel Cressie, Laura Pomeroy, Daniel Janies

^*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Multi-species compartment epidemic models, such as the multi-species susceptible-infectious-recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIR_B) model. Different from a classic multi-species SIR model (which we call MSIR_C), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIR_B model can capture the discrete nature of, as well as uncertainties in, the epidemic process.

Original language	English
Pages (from-to)	457-473
Number of pages	17
Journal	Theoretical Ecology
Volume	6
Issue number	4
DOIs	https://doi.org/10.1007/s12080-013-0180-x
Publication status	Published - Nov 2013
Externally published	Yes

Bibliographical note

Funding Information:
Acknowledgments We would like to express our appreciation to the referees and the editor for their constructive comments, which led to important improvements. Cressie’s research was partially supported by the Naval Surface Warfare Center, Dahlgren Division, under grant number N00178-10-Q-3903. We acknowledge that this material is based upon work supported by, or in part by, the US Army Research Laboratory and Office under grant number W911NF-05-1-0271.

Keywords

Disease dynamics
Epidemic model
HSIR
Influenza
Mass balance
MSIR

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10.1007/s12080-013-0180-x

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title = "Multi-species SIR models from a dynamical Bayesian perspective",

abstract = "Multi-species compartment epidemic models, such as the multi-species susceptible-infectious-recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIRB) model. Different from a classic multi-species SIR model (which we call MSIRC), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIRB model can capture the discrete nature of, as well as uncertainties in, the epidemic process.",

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author = "Lili Zhuang and Noel Cressie and Laura Pomeroy and Daniel Janies",

note = "Funding Information: Acknowledgments We would like to express our appreciation to the referees and the editor for their constructive comments, which led to important improvements. Cressie{\textquoteright}s research was partially supported by the Naval Surface Warfare Center, Dahlgren Division, under grant number N00178-10-Q-3903. We acknowledge that this material is based upon work supported by, or in part by, the US Army Research Laboratory and Office under grant number W911NF-05-1-0271.",

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AU - Pomeroy, Laura

AU - Janies, Daniel

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N2 - Multi-species compartment epidemic models, such as the multi-species susceptible-infectious-recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIRB) model. Different from a classic multi-species SIR model (which we call MSIRC), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIRB model can capture the discrete nature of, as well as uncertainties in, the epidemic process.

AB - Multi-species compartment epidemic models, such as the multi-species susceptible-infectious-recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIRB) model. Different from a classic multi-species SIR model (which we call MSIRC), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIRB model can capture the discrete nature of, as well as uncertainties in, the epidemic process.

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KW - Mass balance

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JO - Theoretical Ecology

JF - Theoretical Ecology

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ER -

Multi-species SIR models from a dynamical Bayesian perspective

Abstract

Bibliographical note

Keywords

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