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.

翻译：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中，其中节点的度分布服从泊松分布。该扩展引入了一个代表平均节点度的额外参数，从而能够纳入接触模式的异质性。利用这一增强模型，我们采用生存分析方法结合哈密顿蒙特卡洛方法，分析了2018-20年刚果民主共和国埃博拉疫情的流行病学数据。我们的结果表明，与传统的仓室模型相比，基于网络的模型能更有效地捕捉流行病动力学的异质性，而无需引入过度复杂的仓室框架。