Stochastic infectious disease models capture uncertainty in public health outcomes and have become increasingly popular in epidemiological practice. However, calibrating these models to observed data is challenging with existing methods for parameter estimation. Stochastic epidemic models are nonlinear dynamical systems with potentially large latent state spaces, resulting in computationally intractable likelihood densities. We develop an approach to calibrating complex epidemiological models to high-dimensional data using Neural Posterior Estimation, a novel technique for simulation-based inference. In NPE, a neural conditional density estimator trained on simulated data learns to "invert" a stochastic simulator, returning a parametric approximation to the posterior distribution. We introduce a stochastic, discrete-time Susceptible Infected (SI) model with heterogeneous transmission for healthcare-associated infections (HAIs). HAIs are a major burden on healthcare systems. They exhibit high rates of asymptotic carriage, making it difficult to estimate infection rates. Through extensive simulation experiments, we show that NPE produces accurate posterior estimates of infection rates with greater sample efficiency compared to Approximate Bayesian Computation (ABC). We then use NPE to fit our SI model to an outbreak of carbapenem-resistant Klebsiella pneumoniae in a long-term acute care facility, finding evidence of location-based heterogeneity in patient-to-patient transmission risk. We argue that our methodology can be fruitfully applied to a wide range of mechanistic transmission models and problems in the epidemiology of infectious disease.

翻译：随机传染病模型能够捕捉公共卫生结果中的不确定性，并在流行病学实践中日益普及。然而，利用现有参数估计方法将这些模型与观测数据进行校准仍具挑战性。随机流行病模型是非线性动力系统，具有潜在巨大的隐状态空间，导致似然密度在计算上难以处理。我们开发了一种基于神经后验估计（一种用于模拟推断的新型技术）的方法，用于将复杂流行病模型与高维数据进行校准。在NPE中，基于模拟数据训练的神经条件密度估计器学习“反转”随机模拟器，返回后验分布的参数化近似。我们引入一个具有异质性传播的随机离散时间易感-感染（SI）模型，用于医疗相关感染（HAIs）。HAIs对医疗系统构成重大负担，且表现出高比例的无症状携带率，这使得感染率估计变得困难。通过大量模拟实验，我们证明与近似贝叶斯计算（ABC）相比，NPE能以更高的样本效率生成准确的感染率后验估计。随后，我们将NPE应用于拟合长期急性护理设施中耐碳青霉烯肺炎克雷伯菌暴发事件，发现患者间传播风险存在基于位置的异质性证据。我们认为，该方法可有效应用于传染病流行病学中的广泛机制传播模型及问题。