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引用次数: 0
摘要
基于个体的传染过程模型有助于预测流行病的轨迹并为干预策略提供信息。在此类模型中,接触网络信息的加入可以捕捉现实接触动态的非随机性和异质性。在本文中,我们考虑在已知的静态网络上对 SIR 传染的传播参数进行贝叶斯推断,其中有关个体疾病状态的信息仅从一系列测试(疾病状态为阳性或阴性)中得知。当传染模型复杂或感染和清除时间等信息缺失时,后验分布可能难以取样。以前的工作曾考虑过使用近似贝叶斯计算(ABC),它允许对复杂模型进行基于模拟的贝叶斯推断。然而,近似贝叶斯计算方法通常要求用户选择合理的汇总统计量。在这里,我们考虑了一种基于混合密度网络压缩 ABC 的推理方案,它能使预期后验熵最小化,从而学习到信息丰富的摘要统计量。这样,我们就能对部分观测到的传染过程参数进行贝叶斯推断,同时也避免了人工选择摘要统计量的需要。这种方法还可以扩展到其他复杂的模拟,包括阳性测试后的行为变化或错误的测试结果。
Flexible Bayesian inference on partially observed epidemics.
Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this article, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC, which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioural change after positive tests or false test results.
期刊介绍:
Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network