论文标题
网络拓扑的有效随机模拟对流行病暴发中感染峰值的影响
Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks
论文作者
论文摘要
本文研究了接触网络对流行病爆发动力学的影响。特别是，我们专注于受到严重感染的节点（PCIN）的峰值数量，确定应保持较低的密集型医疗保健单位的最大工作量。作为模型和仿真方法，我们开发了一个连续的马尔可夫链（CTMC）模型，并基于Gillespie的随机仿真算法（SSA）进行了有效的仿真。这种方法结合了随机过程的现实近似，不依赖于平均场模型的假设和大型的渐近人口大小，如微分方程模型，同时是一种有效的方法来模拟中等大小的网络。根据Covid-19爆发和乌克兰人口数据的数据，分析了不同情况的方法。从这些结果中，我们提取需要考虑有效减少感染峰值的网络拓扑特征。 CTMC模拟在Python中实现，并集成在仪表板中，该仪表板可用于交互式探索，并公开可用。
This paper investigates the effect of the structure of the contact network on the dynamics of the epidemic outbreak. In particular, we focus on the peak number of critically infected nodes (PCIN), determining the maximum workload of intensive healthcare units which should be kept low. As a model and simulation method, we develop a continuous-time Markov chain (CTMC) model and an efficient simulation-based on Gillespie's Stochastic Simulation Algorithm (SSA). This methods combine a realistic approximation of the stochastic process not relying on the assumptions of mean-field models and large asymptotically large population sizes as in differential equation models, and at the same time an efficient way to simulate networks of moderate size. The approach is analysed for different scenarios, based on data from the COVID-19 outbreak and demographic data from Ukraine. From these results we extract network topology features that need to be considered to effectively decrease the peak number of infections. The CTMC simulation is implemented in python and integrated in a dashboard that can be used for interactive exploration and it is made openly available.
