Vyklyuk Yaroslav, Manylich Mykhailo, Škoda Miroslav, Radovanović Milan M, Petrović Marko D
Institute of Laser and Optoelectronic Intelligent Manufacturing, College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China.
Department of Artificial Intelligence at Lviv Polytechnic National University, Lviv, Bandera str, 12, 79013, Ukraine.
Results Phys. 2021 Jan;20:103662. doi: 10.1016/j.rinp.2020.103662. Epub 2020 Dec 9.
Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computer-time and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the high-accuracy matching of the forecast model with real data.
目前，新冠疫情在全球大流行。为评估其流行程度，有必要建立适当的模型，以对国家采取的各种检疫措施的影响进行实时建模。对基于移动元胞自动机的多智能体系统实现的SIR模型进行了改进。本文提出了改进智能体交互和行为规则的方法。已开发出将SIR模型参数与实际地理、社会和医学指标进行比较的方法。这使得能够将新冠疫情的空间分布建模为一个单一地点以及由通过交通相互作用的各个地区组成的整个国家，并考虑公共交通、超市、学校、大学、健身房、教堂、公园等因素。所开发的模型还使我们能够评估检疫措施、对地区间交通连接的限制的影响，考虑潜伏期、口罩制度、人与人之间保持安全距离等因素。该研究开展了一系列实验，从而能够评估阻止疫情的各项单独措施及其综合应用的影响。提出了一种将模型的计算机时间和动态参数与实际数据进行比较的方法，该方法能够评估乌克兰切尔诺夫策地区政府在阻止疫情方面的成效。还对斯洛伐克、土耳其和塞尔维亚等国的疫情传播进行了模拟。计算结果表明预测模型与实际数据高度精确匹配。
