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对政府反应的房室模型和度量的分析研究。

Analytical investigation of compartmental models and measure for reactions of governments.

机构信息

Department of Research and Technology Centers, R &D and Technology Directorate, Turkish Aerospace, 06980, Ankara, Turkey.

出版信息

Eur Phys J E Soft Matter. 2022 Aug 17;45(8):68. doi: 10.1140/epje/s10189-022-00225-y.

DOI:10.1140/epje/s10189-022-00225-y
PMID:35978210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9385104/
Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a major threat to the worldwide health care. In this context, epidemic modelling is an integral part of containment strategies. Compartmental models are typically used for this purpose. Analytical solutions of the two distinct but connected Susceptible-Infectious-Recovered-Deceased (SIRD) and Susceptible-Infectious-Quarantine-Recovered (SIQR) models are presented in this study. Furthermore, the behaviour at the start of a disease outbreak is derived. This analysis shows that a combination of transmission, recovery and isolation rates dominates the behaviour at the start of an epidemic. In addition, the loss occurring due to quarantine and lockdown measures is investigated, where it can be observed that quarantine procedures lead to a smaller loss in comparison with lockdown regulations. Within this framework, optimized strategies that lead to a constant epidemic peak or a minimized loss are presented.

摘要

正在持续的 2019 年冠状病毒病(COVID-19)大流行对全球卫生保健构成了重大威胁。在这种情况下,传染病模型是遏制策略的一个组成部分。为此通常使用房室模型。本研究提出了两种截然不同但相互关联的易感-感染-恢复-死亡(SIRD)和易感-感染-检疫-恢复(SIQR)模型的解析解。此外,还推导出了疾病爆发初期的行为。该分析表明,在疾病爆发初期,传播、恢复和隔离率的组合主导了行为。此外,还研究了由于检疫和封锁措施而导致的损失,结果表明,与封锁规定相比,检疫程序导致的损失较小。在此框架内,提出了导致恒定流行高峰或最小化损失的优化策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/ddc35ac6d43c/10189_2022_225_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/cad86afd9fc9/10189_2022_225_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/ddc35ac6d43c/10189_2022_225_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/67a7a483c3b7/10189_2022_225_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/f7436abbfa03/10189_2022_225_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/9a2bfc0158cc/10189_2022_225_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/9603c0551e3e/10189_2022_225_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/ab2a4ba5b9b0/10189_2022_225_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/b7d3220b4eec/10189_2022_225_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/cad86afd9fc9/10189_2022_225_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/017e/9385104/ddc35ac6d43c/10189_2022_225_Fig8_HTML.jpg

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本文引用的文献

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Use of a Modified SIRD Model to Analyze COVID-19 Data.使用改进的SIRD模型分析新冠肺炎数据。
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A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.
一种用于预测美国县级新冠病毒疾病发病率的时空机器学习方法。
Int J Data Sci Anal. 2023;15(3):247-266. doi: 10.1007/s41060-021-00295-9. Epub 2022 Jan 15.
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Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union.COVID-19 感染的流行病学预测模型:在比荷卢联盟人群中的开发、测试和实施。
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Mathematical analysis of SIRD model of COVID-19 with Caputo fractional derivative based on real data.基于实际数据的具有卡普托分数阶导数的COVID-19 SIRD模型的数学分析
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