标题： 基于粒子MCMC和梯度提议的随机疾病传播模型推断 显示英文标题

标题： Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

摘要： 状态空间模型已被广泛用于通过拟合时间序列数据来模拟目标种群中可传播疾病的动力学。粒子滤波器使这些模型能够包含随机性，从而更好地反映种群行为的真实性质。可以使用粒子MCMC推断相关参数，如疾病的传播、$R_t$和恢复率。标准方法使用Metropolis-Hastings随机游走提议，在存在多个参数时可能难以在合理时间内达到平稳分布。在本文中，我们使用梯度信息和无U型转弯采样器（NUTS）在提出随机非线性易感-暴露-感染-恢复（SEIR）和SIR模型的新参数时获得完整的贝叶斯参数估计。尽管NUTS每次迭代进行多次目标评估，但我们表明它可以在更短的运行时间内提供比Metropolis-Hastings更准确的估计。 显示英文摘要

摘要： State-space models have been widely used to model the dynamics of communicable diseases in populations of interest by fitting to time-series data. Particle filters have enabled these models to incorporate stochasticity and so can better reflect the true nature of population behaviours. Relevant parameters such as the spread of the disease, $R_t$, and recovery rates can be inferred using Particle MCMC. The standard method uses a Metropolis-Hastings random-walk proposal which can struggle to reach the stationary distribution in a reasonable time when there are multiple parameters. In this paper we obtain full Bayesian parameter estimations using gradient information and the No U-Turn Sampler (NUTS) when proposing new parameters of stochastic non-linear Susceptible-Exposed-Infected-Recovered (SEIR) and SIR models. Although NUTS makes more than one target evaluation per iteration, we show that it can provide more accurate estimates in a shorter run time than Metropolis-Hastings.