Background and Objective

The currently active COVID-19 pandemic has increased, among others, public interest in the computational techniques enabling the study of disease-spreading processes. Thus far, numerous approaches have been used to study the development of epidemics, with special attention paid to the identification of crucial elements that can strengthen or weaken the dynamics of the process. The main thread of this research is associated with the use of the ordinary differential equations method. There also exist several approaches based on the analysis of flows in the Cellular Automata (CA) approach.

Methods

In this paper, we propose a new approach to disease-spread modeling. We start by creating a network that reproduces contacts between individuals in a community. This assumption makes the presented model significantly different from the ones currently dominant in the field. It also changes the approach to the act of infection. Usually, some parameters that describe the rate of new infections by taking into account those infected in the previous time slot are considered. With our model, we can individualize this process, considering each contact individually.

Results

The typical output from calculations of a similar type are epidemic curves. In our model, except of presenting the average curves, we show the deviations or ranges for particular results obtained in different simulation runs, which usually lead to significantly different results. This observation is the effect of the probabilistic character of the infection process, which can impact, in different runs, individuals with different significance to the community. We can also easily present the effects of different types of intervention. The effects are studied for different methods used to create the graph representing a community, which can correspond to different social bonds.

Conclusions

We see the potential usefulness of the proposition in the detailed study of epidemic development for specific environments and communities. The ease of entering new parameters enables the analysis of several specific scenarios for different contagious diseases.

中文翻译：

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病毒性疾病在人群中传播。

背景与目的

除其他外，当前活跃的COVID-19大流行已经引起了人们对使能够研究疾病传播过程的计算技术的兴趣。迄今为止，已经使用了许多方法来研究流行病的发展，并特别注意识别可以增强或减弱过程动力学的关键要素。这项研究的主线与使用常微分方程方法有关。基于细胞自动机（CA）方法中的流量分析，还存在几种方法。

方法

在本文中，我们提出了一种新的疾病传播建模方法。我们首先创建一个网络，该网络复制社区中个人之间的联系。该假设使所提出的模型与当前在该领域中占主导地位的模型明显不同。它还改变了感染行为的方法。通常，考虑一些参数，这些参数通过考虑先前时隙中的感染数量来描述新感染的速率。使用我们的模型，我们可以个性化此过程，并单独考虑每个联系人。

结果

相似类型计算的典型输出是流行曲线。在我们的模型中，除了显示平均曲线外，我们还显示了在不同模拟运行中获得的特定结果的偏差或范围，这通常会导致显着不同的结果。该观察结果是感染过程的概率特征的影响，它可以在不同的阶段影响对社区具有不同意义的个体。我们还可以轻松地介绍不同类型的干预措施的效果。研究了用于创建代表社区的图表的不同方法的效果，该方法可以对应于不同的社会纽带。

结论

在针对特定环境和社区的流行病发展的详细研究中，我们看到该提议的潜在有用性。输入新参数的简便性使得可以分析不同传染病的几种特定情况。