具有数据扩充的易感-暴露-感染-恢复流行病模型的贝叶斯推断

IF 1.4 3区社会学 Q3 DEMOGRAPHY

Mathematical Population Studies Pub Date : 2019-09-09 DOI:10.1080/08898480.2019.1656491

Chouaib Beldjoudi, T. Kernane, Hamid El Maroufy

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引用次数: 3

摘要

采用贝叶斯数据增广方法对敏感-暴露-感染-恢复(SEIR)流行病模型的参数进行估计，该模型被表示为连续时间马尔可夫过程，并利用主方程的收敛性近似为扩散过程。利用Euler-Maruyama格式模拟的每对观测值之间的潜在数据点进行估计，除了模型参数外，还需要输入缺失数据。缺失数据和参数被视为随机变量，马尔可夫链蒙特卡罗算法更新缺失数据和参数值。数值仿真结果表明了所提出的马尔可夫链蒙特卡罗算法的有效性。

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Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation

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.

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来源期刊

Mathematical Population Studies 数学-数学跨学科应用

CiteScore

3.20

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions. The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.