标题： 泊松网络 SIR 传染病模型 显示英文标题

标题： Poisson Network SIR Epidemic Model

摘要： 我们将经典的易感-感染-恢复（SIR）模型扩展到一个基于网络的框架，其中节点的度分布遵循泊松分布。 此扩展包含一个额外参数，表示平均节点度，从而可以包含接触模式的异质性。 使用这个增强模型，我们分析了2018-2019年刚果民主共和国埃博拉爆发的流行病数据，采用生存方法结合哈密顿蒙特卡洛方法。 我们的结果表明，与传统分 compartment 模型相比，基于网络的模型能够更有效地捕捉流行病动态的异质性，而不会引入过于复杂的 compartment 框架。 显示英文摘要

摘要： We extend the classical Susceptible-Infected-Recovered (SIR) model to a network-based framework where the degree distribution of nodes follows a Poisson distribution. This extension incorporates an additional parameter representing the mean node degree, allowing for the inclusion of heterogeneity in contact patterns. Using this enhanced model, we analyze epidemic data from the 2018-20 Ebola outbreak in the Democratic Republic of the Congo, employing a survival approach combined with the Hamiltonian Monte Carlo method. Our results suggest that network-based models can more effectively capture the heterogeneity of epidemic dynamics compared to traditional compartmental models, without introducing unduly overcomplicated compartmental framework.