### Abstract

Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.

Original language | English |
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Article number | 100361 |

Journal | Epidemics |

Early online date | 17 Oct 2019 |

DOIs | |

Publication status | First published - 17 Oct 2019 |

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### Bibliographical note

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.### Keywords

- Statistics
- Modelling
- Bayesian
- Spatial
- Infectious diseases

### Cite this

*Epidemics*, [100361]. https://doi.org/10.1016/j.epidem.2019.100361

}

*Epidemics*. https://doi.org/10.1016/j.epidem.2019.100361

**Desirable BUGS in models of infectious diseases.** / Auzenbergs, Megan; Correia-Gomes, Carla; Economou, Theo; Lowe, Rachel; O'Reilly, Kathleen M.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Desirable BUGS in models of infectious diseases

AU - Auzenbergs, Megan

AU - Correia-Gomes, Carla

AU - Economou, Theo

AU - Lowe, Rachel

AU - O'Reilly, Kathleen M

N1 - Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

PY - 2019/10/17

Y1 - 2019/10/17

N2 - Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.

AB - Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.

KW - Statistics

KW - Modelling

KW - Bayesian

KW - Spatial

KW - Infectious diseases

U2 - 10.1016/j.epidem.2019.100361

DO - 10.1016/j.epidem.2019.100361

M3 - Article

C2 - 31668494

JO - Epidemics

JF - Epidemics

SN - 1755-4365

M1 - 100361

ER -