论文标题
暂时网络上SIR模型的快速和原则模拟
Fast and principled simulations of the SIR model on temporal networks
论文作者
论文摘要
易感性感染的（SIR）模型是感染流行病的规范模型，使人们在康复后免疫。计算流行病学中的许多开放问题都涉及基础接触结构对SIR模型等模型的影响。时间网络构成了一个理论框架，该框架能够在谁可以感染谁以及何时发生这些接触的网络中编码结构。在本文中，我们讨论了此类模拟背后的详细假设 - 如何使它们与SIR模型的分析典型公式相提并论，同时又尽可能实现。我们还为此目的提供了高度优化的开源代码，并讨论使程序尽可能快地进行的所有步骤。
The Susceptible-Infectious-Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure's impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations -- how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.
