• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用简单流行病学模型对新冠疫情进行全球分析。

Global analysis of the COVID-19 pandemic using simple epidemiological models.

作者信息

Enrique Amaro José, Dudouet Jérémie, Nicolás Orce José

机构信息

Departamento de Física Atómica, Molecular y Nuclear and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada E-18071, Spain.

Univ Lyon, Univ Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne F-69622, France.

出版信息

Appl Math Model. 2021 Feb;90:995-1008. doi: 10.1016/j.apm.2020.10.019. Epub 2020 Oct 22.

DOI:10.1016/j.apm.2020.10.019
PMID:33110288
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7580557/
Abstract

Several analytical models have been developed in this work to describe the evolution of fatalities arising from coronavirus COVID-19 worldwide. The Death or 'D' model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations - also developed in this work - predict similar trends and suggest a common pandemic evolution with universal parameters. The evolution of the COVID-19 pandemic in several countries shows the typical behavior in concord with our model trends, characterized by a rapid increase of death cases followed by a slow decline, typically asymmetric with respect to the pandemic peak. The fact that the D and ESIR models predict similar results - without and with recovery, respectively - indicates that COVID-19 is a highly contagious virus, but that most people become asymptomatic (D model) and eventually recover (ESIR model).

摘要

在这项工作中,已经开发了几种分析模型来描述全球范围内由冠状病毒COVID-19导致的死亡人数的演变。死亡或“D”模型是著名的SIR(易感-感染-康复) compartment模型的简化版本,它通过假设在疫情期间没有康复,使得传播动力学方程能够通过解析求解。通过拟合现有数据,D模型提供了一种精确的方法来表征疫情演变的指数阶段和正常阶段,并且可以扩展以描述诸如解除封锁措施等额外的时空效应。使用扩展的SIR或ESIR模型(包括康复)进行的更精确计算,以及同样在这项工作中开发出的更复杂的蒙特卡洛网格模拟,预测了相似的趋势,并表明存在具有通用参数的共同疫情演变。几个国家的COVID-19疫情演变显示出与我们模型趋势一致的典型行为,其特征是死亡病例迅速增加,随后缓慢下降,通常相对于疫情高峰不对称。D模型和ESIR模型分别在不考虑康复和考虑康复的情况下预测出相似结果,这一事实表明COVID-19是一种高传染性病毒,但大多数人会无症状(D模型)并最终康复(ESIR模型)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/41f32c11185c/gr14_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/33406a5a0602/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/d29f9f220327/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/3b6a1f8a9f6c/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/17c30e549f03/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/8164356cdbe0/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/6ce48e208c3f/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/178eae98b2d0/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/b9493eb9bcb6/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/26378cd02d5e/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/8c2a8cde89d6/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/3f7e7a2e87d7/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/4b8fc7fef9c9/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/9d7f64152613/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/41f32c11185c/gr14_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/33406a5a0602/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/d29f9f220327/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/3b6a1f8a9f6c/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/17c30e549f03/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/8164356cdbe0/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/6ce48e208c3f/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/178eae98b2d0/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/b9493eb9bcb6/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/26378cd02d5e/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/8c2a8cde89d6/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/3f7e7a2e87d7/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/4b8fc7fef9c9/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/9d7f64152613/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/060f/7580557/41f32c11185c/gr14_lrg.jpg

相似文献

1
Global analysis of the COVID-19 pandemic using simple epidemiological models.使用简单流行病学模型对新冠疫情进行全球分析。
Appl Math Model. 2021 Feb;90:995-1008. doi: 10.1016/j.apm.2020.10.019. Epub 2020 Oct 22.
2
Uncertainty quantification in epidemiological models for the COVID-19 pandemic.新冠疫情流行病学模型中的不确定性量化。
Comput Biol Med. 2020 Oct;125:104011. doi: 10.1016/j.compbiomed.2020.104011. Epub 2020 Sep 25.
3
Monte Carlo simulation of COVID-19 pandemic using Planck's probability distribution.使用普朗克概率分布对 COVID-19 大流行进行蒙特卡罗模拟。
Biosystems. 2022 Aug;218:104708. doi: 10.1016/j.biosystems.2022.104708. Epub 2022 May 27.
4
Forecasting the Long-Term Trends of Coronavirus Disease 2019 (COVID-19) Epidemic Using the Susceptible-Infectious-Recovered (SIR) Model.使用易感-感染-康复(SIR)模型预测2019冠状病毒病(COVID-19)疫情的长期趋势
Infect Dis Rep. 2021 Jul 29;13(3):668-684. doi: 10.3390/idr13030063.
5
Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa.利用疫情数据估算东非地区 COVID-19 动态情况。
BMC Infect Dis. 2022 Jun 9;22(1):531. doi: 10.1186/s12879-022-07510-3.
6
Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm.在惠普高性能计算集群(HPCC)系统平台上使用大数据分析对新冠病毒疾病(Covid-19)病例进行建模和追踪。
J Big Data. 2021;8(1):33. doi: 10.1186/s40537-021-00423-z. Epub 2021 Feb 15.
7
Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic.新冠疫情监测与预测:启发式回归、易感-感染-康复模型及空间随机模型
Front Appl Math Stat. 2021 May 21;7:650716. doi: 10.3389/fams.2021.650716. eCollection 2021.
8
Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.意大利新冠疫情趋势的扩展SIR预测及与中国湖南的比较。
Front Med (Lausanne). 2020 May 6;7:169. doi: 10.3389/fmed.2020.00169. eCollection 2020.
9
A network-based explanation of why most COVID-19 infection curves are linear.基于网络的解释:为什么大多数 COVID-19 感染曲线是线性的。
Proc Natl Acad Sci U S A. 2020 Sep 15;117(37):22684-22689. doi: 10.1073/pnas.2010398117. Epub 2020 Aug 24.
10
Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach.迈向预测新冠病毒感染浪潮:一种随机游走蒙特卡洛模拟方法。
Chaos Solitons Fractals. 2022 Mar;156:111785. doi: 10.1016/j.chaos.2021.111785. Epub 2022 Jan 10.

引用本文的文献

1
Nonlinear mixed models and related approaches in infectious disease modeling: A systematic and critical review.传染病建模中的非线性混合模型及相关方法:系统与批判性综述
Infect Dis Model. 2024 Sep 18;10(1):110-128. doi: 10.1016/j.idm.2024.09.001. eCollection 2025 Mar.
2
Endemic characteristics of SARS-CoV-2 infection.SARS-CoV-2 感染的地方性特征。
Sci Rep. 2023 Sep 8;13(1):14841. doi: 10.1038/s41598-023-41841-8.
3
Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface.

本文引用的文献

1
Impact of non-pharmaceutical interventions against COVID-19 in Europe in 2020: a quasi-experimental non-equivalent group and time series design study.2020 年欧洲 COVID-19 非药物干预的影响:准实验非等同组和时间序列设计研究。
Euro Surveill. 2021 Jul;26(28). doi: 10.2807/1560-7917.ES.2021.26.28.2001401.
2
Analytical features of the SIR model and their applications to COVID-19.SIR模型的分析特征及其在COVID-19中的应用。
Appl Math Model. 2021 Feb;90:466-473. doi: 10.1016/j.apm.2020.08.057. Epub 2020 Sep 28.
3
True epidemic growth construction through harmonic analysis.
改进用于模拟流行病动态的软件并开发用户友好界面。
Infect Dis Model. 2023 Jul 8;8(3):806-821. doi: 10.1016/j.idm.2023.06.003. eCollection 2023 Sep.
4
An approach for joint optimization of probabilistic group test based on cost and time value: taking nucleic acid detection of COVID-19 as an example.一种基于成本和时间价值的概率分组测试联合优化方法:以新冠病毒核酸检测为例。
Soft comput. 2023;27(14):9823-9833. doi: 10.1007/s00500-023-08078-z. Epub 2023 May 23.
5
Effectiveness of government policies in response to the first COVID-19 outbreak.政府应对首次新冠疫情政策的有效性。
PLOS Glob Public Health. 2022 Apr 13;2(4):e0000242. doi: 10.1371/journal.pgph.0000242. eCollection 2022.
6
Modeling the impact of combined use of COVID Alert SA app and vaccination to curb COVID-19 infections in South Africa.建模 COVID Alert SA 应用程序与疫苗接种联合使用对南非 COVID-19 感染的影响。
PLoS One. 2023 Feb 3;18(2):e0264863. doi: 10.1371/journal.pone.0264863. eCollection 2023.
7
Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.建模方法,用于对大流行情况进行预警和监测以及提供决策支持。
Front Public Health. 2022 Nov 14;10:994949. doi: 10.3389/fpubh.2022.994949. eCollection 2022.
8
Systematic description of COVID-19 pandemic using exact SIR solutions and Gumbel distributions.使用精确的SIR模型解和耿贝尔分布对新冠疫情进行系统描述。
Nonlinear Dyn. 2023;111(2):1947-1969. doi: 10.1007/s11071-022-07907-4. Epub 2022 Sep 29.
9
Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive.强调疫苗接种推动的新冠病毒多易感人群模型的全局稳定性及参数敏感性分析
Math Comput Simul. 2023 Jan;203:741-766. doi: 10.1016/j.matcom.2022.07.012. Epub 2022 Jul 23.
10
Airborne transmission of COVID-19 virus in enclosed spaces: An overview of research methods.新冠病毒在封闭空间中的空气传播:研究方法概述。
Indoor Air. 2022 Jun;32(6):e13056. doi: 10.1111/ina.13056.
通过调和分析进行真正的疫情增长构建。
J Theor Biol. 2020 Jun 7;494:110243. doi: 10.1016/j.jtbi.2020.110243. Epub 2020 Mar 12.
4
The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China.新型冠状病毒持续的2019 - nCoV疫情对全球健康构成威胁——中国武汉最新的2019新型冠状病毒爆发。
Int J Infect Dis. 2020 Feb;91:264-266. doi: 10.1016/j.ijid.2020.01.009. Epub 2020 Jan 14.
5
Mathematical epidemiology: Past, present, and future.数学流行病学:过去、现在与未来。
Infect Dis Model. 2017 Feb 4;2(2):113-127. doi: 10.1016/j.idm.2017.02.001. eCollection 2017 May.
6
Seasonality and the effectiveness of mass vaccination.季节性与大规模疫苗接种的效果
Math Biosci Eng. 2016 Apr 1;13(2):249-59. doi: 10.3934/mbe.2015001.
7
Challenges in modelling infectious disease dynamics: preface.传染病动力学建模中的挑战:前言
Epidemics. 2015 Mar;10:iii-iv. doi: 10.1016/j.epidem.2015.02.001. Epub 2015 Feb 11.
8
Modeling infectious disease dynamics in the complex landscape of global health.在全球健康复杂格局中建模传染病动态。
Science. 2015 Mar 13;347(6227):aaa4339. doi: 10.1126/science.aaa4339.
9
Basic models for disease occurrence in epidemiology.
Int J Epidemiol. 1995 Feb;24(1):1-7. doi: 10.1093/ije/24.1.1.
10
Discussion: the Kermack-McKendrick epidemic threshold theorem.讨论:克尔马克-麦肯德里克流行阈值定理。
Bull Math Biol. 1991;53(1-2):3-32. doi: 10.1016/s0092-8240(05)80039-4.