2019年冠状病毒病(COVID-19)
计算机科学
大流行
干预(咨询)
管理科学
离散事件仿真
心理干预
风险分析(工程)
宏
运筹学
数据科学
系统工程
模拟
工程类
心理学
医学
精神科
病理
程序设计语言
传染病(医学专业)
疾病
作者
Weiwei Zhang,Shiyong Liu,Nathaniel Osgood,Hongli Zhu,Ying Qian,Peng Jia
摘要
Abstract This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.
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