ABSTRACT A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.

中文翻译：

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具有数据增强的易感-暴露-感染-恢复流行病模型的贝叶斯推理

摘要 贝叶斯数据增强方法允许估计易感暴露感染恢复 (SEIR) 流行病模型中的参数，该模型被表述为连续时间马尔可夫过程，并通过使用主方程收敛的扩散过程进行近似。估计是使用通过 Euler-Maruyama 方案模拟的每对观测之间的潜在数据点进行的，该方案涉及除了模型参数之外还输入缺失数据。缺失数据和参数被视为随机变量，马尔可夫链蒙特卡洛算法更新缺失数据和参数值。数值模拟显示了所提出的马尔可夫链蒙特卡洛算法的有效性。