The new coronavirus SARS‐COV‐2 is currently impacting life around the globe.1 The rapid spread of this viral disease might be highly challenging for health care systems. This was seen in Northern Italy and in New York City for example.2 Governments reacted with different measures such as shutdown of all schools, universities and up to a general curfew. All of those measures have a huge impact on the economy. The United Nations secretary general has stated recently: “The COVID‐19 pandemic is one of the most dangerous challenges this world has faced in our lifetime. It is above all a human crisis with severe health and socio‐economic consequences”.3 According to the European mortality observing network4: “Pooled mortality estimates from the EuroMOMO network continue to show a markedly increased level of excess all‐cause mortality overall for the participating European countries, coinciding with the current COVID‐19 pandemic” (see Figure 1).

There are many aspects that need to be considered when considering measures such as lockdowns. One of the main problems is that aspects of the dynamical behaviour are hard to grasp with our thinking in the context of our experiences. Our understanding of cause and relationship is mostly related to “linear thinking”. This means that there is a linear relationship between a potential influence and a result. An example would be “If you buy twice as many apples you have to pay twice the amount of money.” This concept however does not work for exponential growth rates. Those are governed by rules that are highly sensitive to the conditions of the underlying process. In the context of a viral disease we can consider the concept of growth rate describing the factor that describes the number of affected individuals divided by the number the previous day. For example, if you change the growth rate in an ideal exponential scenario from 1.1 by 10% to 1.21 you will get after only 10 days more than twice and after 20 days more than six times the amount (see Figure 2). In the case of COVID‐19 it has been estimated that the reproductive number can be as large as 3.95 in this particular study. The exponential growth however could only be found in a situation where there are an infinite number of individuals that can get viral disease. This is obviously not the case. Exponential‐like growth can be observed only in the beginning of the spread, where the number of people who do not have the disease is a lot bigger in comparison to the infectious individuals. In order to understand the spread and aspects of the dynamics of the disease we can use a modelling approach.6

Here we explore an agent‐based model where dots as surrogates for individuals move around in a given space. The simulation can be found at https://pansim.uni‐jena.de. Each of the dots has one of the following states: susceptible, infectious without symptoms, infectious with symptoms, recovered and immune. Once infected, the dots go through the disease cycle described by the states. The dots move around in the space with a given “mobility”. Once the dots get aware of the disease they change their mobility.

The ultimate goal would be to eradicate the SARS‐2 virus worldwide. This is unlikely to achieve on a short‐term scale because of its pandemic characteristic. When the pandemic runs through the population a key figure is the maximum number of active cases. Since a certain percentage of the active cases will require intensive care the maximum number of active cases determines whether or not the healthcare system runs at its limit or not.

In order to model different theoretical scenarios, we have implemented predefined parameter settings. The different scenarios described in the following relate a “default” parameter setting that is used to compare the modifications.

新型冠状病毒 SARS-COV-2 目前正在影响全球的生活。1这种病毒性疾病的迅速传播可能对医疗保健系统构成极大挑战。例如，在意大利北部和纽约市就可以看到这种情况。2各国政府采取了不同的措施，例如关闭所有学校、大学，甚至实行全面宵禁。所有这些措施都对经济产生了巨大影响。联合国秘书长最近表示：“COVID-19 大流行是这个世界在我们有生之年面临的最危险挑战之一。这首先是一场具有严重健康和社会经济后果的人类危机”。3根据欧洲死亡率观察网络4：“来自 EuroMOMO 网络的综合死亡率估计继续显示，参与欧洲国家的总体全因死亡率显着增加，与当前的 COVID-19 大流行相吻合”（见图 1）。

在考虑封锁等措施时，需要考虑许多方面。主要问题之一是动态行为的各个方面很难在我们的经验背景下用我们的思维来掌握。我们对因果关系的理解大多与“线性思维”有关。这意味着潜在影响与结果之间存在线性关系。一个例子是“如果你买两倍的苹果，你必须支付两倍的钱。” 然而，这个概念不适用于指数增长率。这些规则受对基础过程条件高度敏感的规则的约束。在病毒性疾病的背景下，我们可以考虑增长率的概念，该概念描述了描述受影响个体数量除以前一天数量的因素。例如，如果您将理想指数情景中的增长率从 1.1 更改 10% 到 1.21，则仅 10 天后您将获得两倍以上的数量，而 20 天后将获得六倍以上的数量（参见图 2）。在 COVID-19 的情况下，据估计繁殖数可高达 3.95在这项特定的研究中。然而，指数增长只能在有无限数量的个体可能感染病毒性疾病的情况下才能找到。显然情况并非如此。只有在传播开始时才能观察到指数式增长，与感染者相比，没有患病的人数要多得多。为了了解疾病动态的传播和方面，我们可以使用建模方法。6

在这里，我们探索了一个基于代理的模型，其中点作为个体的代理在给定空间中移动。模拟可以在 https://pansim.uni-jena.de 找到。每个点都具有以下状态之一：易感、无症状感染、有症状感染、康复和免疫。一旦被感染，这些点就会经历各州描述的疾病周期。这些点在空间中以给定的“流动性”移动。一旦这些圆点意识到这种疾病，它们就会改变它们的流动性。

最终目标是在全球范围内根除 SARS-2 病毒。由于其大流行的特点，这不太可能在短期内实现。当大流行在人群中蔓延时，一个关键数字是活跃病例的最大数量。由于一定百分比的活动病例需要重症监护，因此活动病例的最大数量决定了医疗保健系统是否在其极限运行。

为了模拟不同的理论场景，我们实现了预定义的参数设置。以下描述的不同场景涉及用于比较修改的“默认”参数设置。