Alvarez F P, Crépey P, Barthélemy M, Valleron A-J
INSERM, U707, ESIM, Paris, France.
Methods Inf Med. 2007;46(1):19-26.
We present a simulation software which allows studying the dynamics of a hypothetic infectious disease within a network of connected people. The software is aimed to facilitate the discrimination of stochastic factors governing the evolution of an infection in a network. In order to do this it provides simple tools to create networks of individuals and to set the epidemiological parameters of the outbreaks.
Three popular models of infectious disease can be used (SI, SIS, SIR). The simulated networks are either the algorithm-based included ones (scale free, small-world, and random homogeneous networks), or provided by third party software.
It allows the simulation of a single or many outbreaks over a network, or outbreaks over multiple networks (with identical properties). Standard outputs are the evolution of the prevalence of the disease, on a single outbreak basis or by averaging many outbreaks. The user can also obtain customized outputs which address in detail different possible epidemiological questions about the spread of an infectious agent in a community.
The presented software introduces sources of stochasticity present in real epidemics by simulating outbreaks on contact networks of individuals. This approach may help to understand the paths followed by outbreaks in a given community and to design new strategies for preventing and controlling them.
我们展示了一款模拟软件，它能够研究传染病在相互连接的人群网络中的动态变化。该软件旨在促进对控制网络中感染传播的随机因素的辨别。为实现此目的，它提供了创建个体网络以及设置疫情流行病学参数的简单工具。
可使用三种流行的传染病模型（SI、SIS、SIR）。模拟网络既可以是基于算法的内置网络（无标度、小世界和随机均匀网络），也可以由第三方软件提供。
它能够模拟网络上的单次或多次疫情爆发，或者多个网络（具有相同属性）上的疫情爆发。标准输出是疾病患病率的演变情况，基于单次疫情爆发或通过对多次疫情爆发求平均值得出。用户还可以获得定制输出，这些输出详细解答了关于传染病在社区传播的不同可能的流行病学问题。
所展示的软件通过在个体接触网络上模拟疫情爆发，引入了实际疫情中存在的随机因素来源。这种方法可能有助于理解给定社区中疫情爆发所遵循的路径，并设计预防和控制疫情的新策略。
