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COVID-19 的新型隔室模型。

New compartment model for COVID-19.

机构信息

Kyushu University, Fukuoka, 819-0395, Japan.

Research Institute for Science Education, Inc., Kyoto, 603-8346, Japan.

出版信息

Sci Rep. 2023 Apr 3;13(1):5409. doi: 10.1038/s41598-023-32159-6.

DOI:10.1038/s41598-023-32159-6
PMID:37012332
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10068699/
Abstract

The SIR or susceptible-infected-recovered model is the standard compartment model for understanding epidemics and has been used all over the world for COVID-19. While the SIR model assumes that infected patients are identical to symptomatic and infectious patients, it is now known that in COVID-19 pre-symptomatic patients are infectious and there are significant number of asymptomatic patients who are infectious. In this paper, population is separated into five compartments for COVID-19; susceptible individuals (S), pre-symptomatic patients (P), asymptomatic patients (A), quarantined patients (Q) and recovered and/or dead patients (R). The time evolution of population in each compartment is described by a set of ordinary differential equations. Numerical solution to the set of differential equations shows that quarantining pre-symptomatic and asymptomatic patients is effective in controlling the pandemic.

摘要

SIR 或易感-感染-恢复模型是理解传染病的标准隔室模型,已在全球范围内用于 COVID-19。虽然 SIR 模型假设感染患者与有症状和传染性患者相同,但现在已知在 COVID-19 中,有症状前患者具有传染性,并且有相当数量的无症状患者具有传染性。在本文中,人群被分为 COVID-19 的五个隔室;易感个体 (S)、有症状前患者 (P)、无症状患者 (A)、隔离患者 (Q) 和康复和/或死亡患者 (R)。每个隔室中的人群随时间的演化由一组常微分方程描述。微分方程组的数值解表明,对有症状前和无症状患者进行隔离在控制大流行方面是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/406e15684d36/41598_2023_32159_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/426fd2114ce6/41598_2023_32159_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/018481bae7b0/41598_2023_32159_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/1046f19d7ede/41598_2023_32159_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/8d1d3e34dd9c/41598_2023_32159_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/406e15684d36/41598_2023_32159_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/426fd2114ce6/41598_2023_32159_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/018481bae7b0/41598_2023_32159_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/1046f19d7ede/41598_2023_32159_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/8d1d3e34dd9c/41598_2023_32159_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ae5/10070410/406e15684d36/41598_2023_32159_Fig5_HTML.jpg

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本文引用的文献

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Prevalence of Asymptomatic SARS-CoV-2 Infection in Japan.日本无症状 SARS-CoV-2 感染的流行率。
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An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation.评估美国前瞻性 COVID-19 建模研究:从数据到科学转化。
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SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19.
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Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities.为许多国家、州和城市估计和模拟新冠疫情的易感-感染-康复-死亡(SIRD)模型。
J Econ Dyn Control. 2022 Jul;140:104318. doi: 10.1016/j.jedc.2022.104318. Epub 2022 Jan 29.
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A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19.对用于新型冠状病毒肺炎(COVID-19)研究、预测和管理的数学建模、人工智能及数据集的综述。
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Magnitude of asymptomatic COVID-19 cases throughout the course of infection: A systematic review and meta-analysis.无症状 COVID-19 病例在整个感染过程中的严重程度:系统评价和荟萃分析。
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