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  • 标题:K-SEIR-Sim: A simple customized software for simulating the spread of infectious diseases
  • 本地全文:下载
  • 作者:Hongzhi Wang ; Zhiying Miao ; Chaobao Zhang
  • 期刊名称:Computational and Structural Biotechnology Journal
  • 印刷版ISSN:2001-0370
  • 出版年度:2021
  • 卷号:19
  • 页码:1966-1975
  • DOI:10.1016/j.csbj.2021.04.004
  • 出版社:Computational and Structural Biotechnology Journal
  • 摘要:Infectious disease is a great enemy of humankind. The ravages of COVID-19 are leading to profound crises across the world. There is an urgent requirement for analyzing the current pandemic situation, predicting trends over time, and assessing the effectiveness of containment measures. Thus, numerous statistical models, primarily based on the susceptible–exposed–infected–recovered or removed (SEIR) model, have been established. However, these models are highly technical, which are difficult for the public and governing bodies to understand and use. To address this issue, we developed a simple operating software based on our improved K-SEIR model termed as the kernelkernel SEIR simulator (K-SEIR-Sim). This software includes natural propagation parameters, containment measure parameters, and certain characteristic parameters that can deduce the effects of natural propagation and containment measures. Further, the applicability of the proposed software was demonstrated using the example of the COVID-19 outbreak in the United States and the city of Wuhan, China. Operating results verified the potency of the proposed software in evaluating the epidemic situation and human intervention during COVID-19. Importantly, the software can perform real-time, backward-looking, and forward-looking analysis by functioning in data-driven and model-driven ways. All of them have considerable practical values in their applications according to the actual needs of personal use. Conclusively, K-SEIR-Sim is the first simple customized operating software that is highly valuable for the global fight against COVID-19 and other infectious diseases.
  • 关键词:Software ; Artificial intelligence ; Python ; COVID-19 ; SEIR model ; Simulation analysis ; 2019-nCoV
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