论文标题
使用粒子MCMC和基于梯度的建议推理随机疾病传播模型
Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
论文作者
论文摘要
州空间模型已被广泛用于模拟感兴趣的人群中传染病的动态，并拟合时间序列数据。粒子过滤器使这些模型能够结合随机性，因此可以更好地反映人口行为的真实本质。可以使用粒子MCMC来推断疾病，$ R_T $的传播，$ R_T $和恢复率等相关参数。标准方法使用大都市杂货随机步行提案，在有多个参数时，可能会在合理的时间内努力达到固定分布。 在本文中，我们在提出随机非线性非线性易感暴露感染的（SEIR）和SIR模型的新参数时，使用梯度信息和无掉头采样器（螺母）获得完整的贝叶斯参数估计。尽管Nuts每次迭代进行了多个目标评估，但我们表明，它可以在较短的运行时间内提供比大都市狂热的更准确的估计。
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.
