School of Science, Mathematical Sciences Discipline, RMIT University, Melbourne, 3000, Victoria, Australia.
Spat Spatiotemporal Epidemiol. 2024 Jun;49:100645. doi: 10.1016/j.sste.2024.100645. Epub 2024 Feb 28.
Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.
贝叶斯推断在使用贝叶斯推断使用 Gibbs 抽样 (BUGS) 的传染病建模中，在过去二十年中与计算和模型开发的进步并行显著。BUGS 能够轻松实现马尔可夫链蒙特卡罗 (MCMC) 方法，使贝叶斯分析成为传染病建模的主流。然而，现有的运行 MCMC 进行贝叶斯推断的软件在计算复杂性方面存在挑战，尤其是当传染病模型变得更加复杂，具有时空成分，以及参数和大型数据集数量增加时。本研究调查了在 Just Another Gibbs Sampler (JAGS) 环境中创建模型的两种替代下标策略及其在运行时间方面的性能。我们的研究结果对于确保贝叶斯时空传染病建模的效率和及时实施具有实用价值。
