用复杂系统的随机描述方法建模流行病

IF 0.9 Q3 MATHEMATICS, APPLIED
Bruno Carbonaro
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引用次数: 2

摘要

本文的目的是展示一种方法,通过在玻尔兹曼动力学理论的框架中描述流行病,可以解决预测流行病演变的问题,因为它在过去几年中已经发展并应用于复杂系统,通过对玻尔兹曼方程的适当修改,通过对状态变量的适当重新解释和引入“功能子系统”的概念。因此,在本文中,我们将任意(国家)人口S建模为一个复杂系统,分为两个功能子系统,第一个包含S的所有单个个体,第二个包含“护理工具”,这意味着医院中有足够数量的医生和设备进行重症监护。第一个子系统上的状态变量将是“健康状态”,另一个子系统上的状态变量将是“有效性”。然后,我们将写出一个非线性常微分方程组,它给出了健康状态可能值集合上的概率分布的演化。通过分配部分基于合理性假设的数据,部分根据坎帕尼亚地区机构提供的数据进行估计,该系统采用了一种允许对这种演变进行数值模拟的形式,这将在即将发表的论文中进行并提出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling epidemics by means of the stochastic description of complex systems

The aim of this article is to show a way in which the problem of predicting the evolution of an epidemic may be tackled by describing it in the framework of Boltzmann's kinetic theory, as it has been developed and applied in the last years to complex systems by a suitable modification of the Boltzmann equation, via a suitable reinterpretation of state variables and the introduction of the notion of «functional subsystems». Accordingly, in this article we model an arbitrary (national) population S as a complex system, split in two functional subsystems, the first containing all single individuals of S and the second containing the «care tools», that are to be meant as available places in hospitals with a sufficient number of physicians and of equipments for intensive cares. The state variable on the first subsystem will be the «health state», and the state variable on the other will be the «effectiveness». We shall then write a system of nonlinear ordinary differential equations which gives the evolution of the probability distribution on the set of possible values of the health states. By assigning data partly on the basis of plausibility assumptions and partly as estimated from those furnished by institutions of Campania region, the system takes a form allowing the numerical simulation of such evolution, which will be performed and presented in a forthcoming paper.

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CiteScore
2.20
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