An agent-based model to evaluate the COVID-19 transmission risks in facilities
An agent-based model to evaluate the COVID-19 transmission risks in facilities
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DOI：
10.1016/j.compbiomed.2020.103827
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发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
Cuevas, Erik
中科院分区：
文献类型：
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作者：
Cuevas, Erik
冠状病毒病(新冠肺炎)的迅速蔓延已成为一种全球性威胁，影响到世界上几乎所有国家。随着各国达到感染高峰，计划在不同共存条件下回归新常态，以减少公司、大学、商店等全部或部分关闭所产生的经济影响。在这种情况下，利用数学模型评估新冠肺炎在各种设施中的传播风险是协助当局做出知情决策的重要工具。另一方面，基于代理的建模是一种相对较新的方法来建模由代理组成的复杂系统，这些代理的行为是使用简单的规则来描述的。与经典的数学模型(考虑同质种群)不同，基于智能体的方法对具有不同特征的个体进行建模，并提供更真实的结果。提出了一种基于智能体的设施内新冠肺炎传播风险评估模型。所提出的方案被设计用于模拟时空传输过程。在该模型中，模拟的智能体根据编程的规则进行决策。这些规则对应于病原体相互作用以表征传播过程的空间模式和感染条件。该模型还包括每个代理的个人配置文件，其中定义了其在交互过程中使用的主要社会特征和健康状况。通常，此配置文件部分决定了代理在与其他个人交互期间的行为。考虑了几个假设场景来显示所提出的模型的性能。实验结果表明，仿真为制定降低新冠肺炎在设施内传播风险的策略提供了有用的信息。
The rapid spread of the coronavirus disease (COVID-19) has become a global threat affecting almost all countries in the world. As countries reach the infection peak, it is planned to return to a new normal under different coexistence conditions in order to reduce the economic effects produced by the total or partial closure of companies, universities, shops, etc. Under such circumstances, the use of mathematical models to evaluate the transmission risk of COVID-19 in various facilities represents an important tool in assisting authorities to make informed decisions. On the other hand, agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. Different from classical mathematical models (which consider a homogenous population), agent-based approaches model individuals with distinct characteristics and provide more realistic results. In this paper, an agent-based model to evaluate the COVID-19 transmission risks in facilities is presented. The proposed scheme has been designed to simulate the spatiotemporal transmission process. In the model, simulated agents make decisions depending on the programmed rules. Such rules correspond to spatial patterns and infection conditions under which agents interact to characterize the transmission process. The model also includes an individual profile for each agent, which defines its main social characteristics and health conditions used during its interactions. In general, this profile partially determines the behavior of the agent during its interactions with other individuals. Several hypothetical scenarios have been considered to show the performance of the proposed model. Experimental results have demonstrated that the simulations provide useful information to produce strategies for reducing the transmission risks of COVID-19 within the facilities.