Accelerating Bayesian inference for stochastic epidemic models using incidence data
Accelerating Bayesian inference for stochastic epidemic models using incidence data
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DOI：
10.1007/s11222-023-10311-6
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发表时间：
2023-12-01
影响因子：
2.2
通讯作者：
Kypraios，Theodore
中科院分区：
文献类型：
--
作者：
Golightly，Andrew;Wadkin，Laura E.;Kypraios，Theodore
我们考虑的情况下，随机流行病隔间模型进行贝叶斯推断，使用不完整的时间过程数据组成的发病率计数，无论是新的感染或清除的数量在固定长度的时间间隔。我们避开了最自然的马尔可夫跳跃过程表示的计算效率的原因，并专注于一个随机微分方程表示。这是进一步近似给出一个易于处理的高斯过程，即线性噪声近似（LNA）。除非将LNA连接到数据的观察模型是线性和高斯的，否则观察到的数据可能性仍然难以处理。正是在这种情况下，我们考虑两种方法的边缘化的潜在过程：一个相关的伪边缘的方法和分析边缘化通过高斯近似的观察模型。我们比较和对比这些方法，使用合成数据，然后应用最佳的执行方法的真实的数据组成的去除发病率的橡树游行蛾巢在里士满公园，伦敦。我们的方法进一步允许各种竞争隔室模型之间的比较。
We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed length. We eschew the most natural Markov jump process representation for reasons of computational efficiency, and focus on a stochastic differential equation representation. This is further approximated to give a tractable Gaussian process, that is, the linear noise approximation (LNA). Unless the observation model linking the LNA to data is both linear and Gaussian, the observed data likelihood remains intractable. It is in this setting that we consider two approaches for marginalising over the latent process: a correlated pseudo-marginal method and analytic marginalisation via a Gaussian approximation of the observation model. We compare and contrast these approaches using synthetic data before applying the best performing method to real data consisting of removal incidence of oak processionary moth nests in Richmond Park, London. Our approach further allows comparison between various competing compartment models.