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，在流行病学实践中日益受到重视。然而，利用现有参数估计方法将这些模型校准至观测数据具有挑战性。随机流行病模型是非线性动力系统，其潜在状态空间可能非常庞大，导致似然密度在计算上难以处理。我们提出了一种利用神经后验估计（一种基于模拟推断的新技术）将复杂流行病学模型校准至高维数据的方法。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。我们针对医疗相关感染（HAIs）引入了一个具有异质性传播的随机离散时间易感者-感染者（SI）模型。HAIs是医疗系统的重大负担，其表现出高比例的渐近携带状态，使得感染率估计变得困难。通过大量模拟实验，我们证明相较于近似贝叶斯计算（ABC），NPE能以更高的样本效率生成准确的感染率后验估计。随后，我们运用NPE将SI模型拟合至某长期急性护理机构中耐碳青霉烯类肺炎克雷伯菌的暴发数据，发现了患者间传播风险存在基于地理位置异质性的证据。我们认为，该方法可有效应用于广泛的机械传播模型及传染病流行病学问题。