Research Institute of Industrial Technology Convergence, Korea Institute of Industrial Technology (KITECH), Ansan 15588, Korea.
Department of Computer Engineering, Korea University of Technology and Education (KOREATECH), Cheonan 31253, Korea.
Int J Environ Res Public Health. 2021 Oct 27;18(21):11264. doi: 10.3390/ijerph182111264.
This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.
本研究利用建模和模拟来分析政府政策对冠状病毒（COVID-19）感染趋势的影响。在感染模拟中考虑了两种建模要求：（1）实施社会隔离政策，（2）表示人口流动。为此，我们提出了一种扩展的感染模型，将分析模型与基于离散事件的模拟模型以混合形式结合在一起。社会隔离政策的模拟参数被识别并嵌入到分析模型中。行政区被建模为基本的模拟代理，便于在城市之间表示人口流动。所提出的感染模型利用了韩国有关疑似、感染、康复和死亡人员的实际数据。作为应用，我们模拟了韩国的 COVID-19 疫情。我们使用了包含开始实施隔离政策和将隔离政策调整到下一阶段的有意义的 160 天的实际数据。我们期望所提出的工作在分析社会隔离如何有效影响病毒预防方面发挥主要作用，并为生化领域提供一个模拟环境。
