Similarity of epidemic spreading and information network connectivity mechanisms demonstrated by analysis of two probabilistic models

IF 1.1 Q4 BIOPHYSICS
Into Almiala, Vesa Kuikka
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引用次数: 3

Abstract

The modelling of epidemic spreading is essential in understanding the mechanisms of outbreaks and pandemics. Many models for different kinds of spreading have been proposed throughout the history of modelling, each suited for a specific scenario and parameters. On the other hand, models of information networks provide important tools for the analysis of the performance and reliability of such networks. We have previously presented a model for simulating the spreading of infectious disease throughout a social network and another one for simulating the connectivity of data traffic in an information network. We argue that these models are similar in that they produce equivalent results with appropriate parameters when run on the same network. We explain this by reasoning that the manners in which the models carry out their calculations, although devised from different assumptions, turn out to be equivalent. We also show empirical results of applying the models to calculate the spread of contagion and information connectivity on two complex networks suitable for the models. Based on the results, we calculate centrality metrics reflecting the outcome of the application, highlighting its important properties. We note that the centrality values obtained by running the epidemic model and the connectivity model turn out to be mutually equivalent, as predicted by their similar fashions of calculation. As the models were independently developed for their own applications, the equivalence in their calculation can not be explained by the models purposefully built similarly. Thus, not only are the two apparently completely separate areas of interest analysable with a single model but there appear to be inherent similarities in the mechanisms of epidemic spreading and determining network connectivity.
通过对两种概率模型的分析,证明了疫情传播和信息网络连接机制的相似性
建立流行病传播模型对于理解疾病爆发和大流行的机制至关重要。在整个建模历史中,已经提出了许多不同类型的扩散模型,每个模型都适合于特定的情景和参数。另一方面,信息网络模型为分析信息网络的性能和可靠性提供了重要的工具。我们之前已经提出了一个模拟传染病在整个社会网络中传播的模型,以及另一个模拟信息网络中数据流量连通性的模型。我们认为这些模型是相似的,因为它们在相同的网络上运行时,在适当的参数下产生等效的结果。我们通过推理来解释这一点,即模型进行计算的方式,尽管是从不同的假设中设计出来的,但结果是等效的。我们还展示了应用该模型在两个适合该模型的复杂网络上计算传染传播和信息连通性的实证结果。基于结果,我们计算反映应用程序结果的中心性度量,突出其重要属性。我们注意到,通过运行流行病模型和连通性模型得到的中心性值是相互等效的,正如它们相似的计算方式所预测的那样。由于这些模型是为各自的应用而独立开发的,它们计算中的等效性不能用有目的地建立相似的模型来解释。因此,不仅可以用单一模型分析这两个显然完全独立的领域,而且在流行病传播和决定网络连通性的机制方面似乎也存在固有的相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
AIMS Biophysics
AIMS Biophysics BIOPHYSICS-
CiteScore
2.40
自引率
20.00%
发文量
16
审稿时长
8 weeks
期刊介绍: AIMS Biophysics is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of biophysics. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Biophysics welcomes, but not limited to, the papers from the following topics: · Structural biology · Biophysical technology · Bioenergetics · Membrane biophysics · Cellular Biophysics · Electrophysiology · Neuro-Biophysics · Biomechanics · Systems biology
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