数学流行病学原理及 compartmental 模型在 COVID-19 中的应用 - Suppr

Suppr

超能文献

Principles of mathematical epidemiology and compartmental modelling application to COVID-19.

作者信息

Reyné Bastien, Saby Nicolas, Sofonea Mircea T

机构信息

MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.

IMAG, Université de Montpellier, CNRS, Montpellier, France.

出版信息

Anaesth Crit Care Pain Med. 2022 Feb;41(1):101017. doi: 10.1016/j.accpm.2021.101017. Epub 2021 Dec 28.

DOI:10.1016/j.accpm.2021.101017

PMID:34971801

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8713430/

Abstract

摘要

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e17b/8713430/c9d9762c0df5/gr1_lrg.jpg

相似文献

Principles of mathematical epidemiology and compartmental modelling application to COVID-19.

Anaesth Crit Care Pain Med. 2022 Feb;41(1):101017. doi: 10.1016/j.accpm.2021.101017. Epub 2021 Dec 28.

Analysis of a COVID-19 compartmental model: a mathematical and computational approach.

Math Biosci Eng. 2021 Sep 14;18(6):7979-7998. doi: 10.3934/mbe.2021396.

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study.

Math Biosci Eng. 2021 Oct 15;18(6):8905-8932. doi: 10.3934/mbe.2021439.

Use of the Hayami diffusive wave equation to model the relationship infected-recoveries-deaths of Covid-19 pandemic.

Epidemiol Infect. 2021 Apr 29;149:e138. doi: 10.1017/S0950268821001011.

Optimal control strategies for the transmission risk of COVID-19.

J Biol Dyn. 2020 Dec;14(1):590-607. doi: 10.1080/17513758.2020.1788182.

A mathematical model for human-to-human transmission of COVID-19: a case study for Turkey's data.

Math Biosci Eng. 2021 Nov 5;18(6):9787-9805. doi: 10.3934/mbe.2021480.

SARS-CoV-2 and self-medication in Cameroon: a mathematical model.

J Biol Dyn. 2021 Dec;15(1):137-150. doi: 10.1080/17513758.2021.1883130.

Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19.

Front Public Health. 2021 Feb 12;9:593417. doi: 10.3389/fpubh.2021.593417. eCollection 2021.

A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics.

Math Biosci Eng. 2022 Jan;19(2):1213-1238. doi: 10.3934/mbe.2022056. Epub 2021 Dec 1.

SARS-CoV-2 Serial Interval Variation, Montana, USA, March 1-July 31, 2020.

Emerg Infect Dis. 2021 May;27(5):1486-1491. doi: 10.3201/eid2705.204663.

引用本文的文献

Operational stability study of lactate biosensors: modeling, parameter identification, and stability analysis.

Front Bioeng Biotechnol. 2024 Jul 16;12:1385459. doi: 10.3389/fbioe.2024.1385459. eCollection 2024.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

PLoS Comput Biol. 2024 May 17;20(5):e1012124. doi: 10.1371/journal.pcbi.1012124. eCollection 2024 May.

Epidemic models: why and how to use them.

Anaesth Crit Care Pain Med. 2022 Apr;41(2):101048. doi: 10.1016/j.accpm.2022.101048. Epub 2022 Feb 28.

本文引用的文献

Emerging dynamics from high-resolution spatial numerical epidemics.

Elife. 2021 Oct 15;10:e71417. doi: 10.7554/eLife.71417.

Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France.

Anaesth Crit Care Pain Med. 2021 Aug;40(4):100943. doi: 10.1016/j.accpm.2021.100943.

Memory is key in capturing COVID-19 epidemiological dynamics.

Epidemics. 2021 Jun;35:100459. doi: 10.1016/j.epidem.2021.100459. Epub 2021 Apr 27.

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic.

PLoS Comput Biol. 2021 Mar 4;17(3):e1008776. doi: 10.1371/journal.pcbi.1008776. eCollection 2021 Mar.

Opinion: What models can and cannot tell us about COVID-19.

Proc Natl Acad Sci U S A. 2020 Jul 14;117(28):16092-16095. doi: 10.1073/pnas.2011542117. Epub 2020 Jun 24.

Estimating the burden of SARS-CoV-2 in France.

Science. 2020 Jul 10;369(6500):208-211. doi: 10.1126/science.abc3517. Epub 2020 May 13.

Special report: The simulations driving the world's response to COVID-19.

Nature. 2020 Apr;580(7803):316-318. doi: 10.1038/d41586-020-01003-6.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks.

Epidemics. 2019 Dec;29:100356. doi: 10.1016/j.epidem.2019.100356. Epub 2019 Aug 26.

Inferring R0 in emerging epidemics-the effect of common population structure is small.

J R Soc Interface. 2016 Aug;13(121). doi: 10.1098/rsif.2016.0288.

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型，支持多种主流文档格式。

立即体验

相似文献

Principles of mathematical epidemiology and compartmental modelling application to COVID-19.

Anaesth Crit Care Pain Med. 2022 Feb;41(1):101017. doi: 10.1016/j.accpm.2021.101017. Epub 2021 Dec 28.

Analysis of a COVID-19 compartmental model: a mathematical and computational approach.

Math Biosci Eng. 2021 Sep 14;18(6):7979-7998. doi: 10.3934/mbe.2021396.

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study.

Math Biosci Eng. 2021 Oct 15;18(6):8905-8932. doi: 10.3934/mbe.2021439.

Use of the Hayami diffusive wave equation to model the relationship infected-recoveries-deaths of Covid-19 pandemic.

Epidemiol Infect. 2021 Apr 29;149:e138. doi: 10.1017/S0950268821001011.

Optimal control strategies for the transmission risk of COVID-19.

J Biol Dyn. 2020 Dec;14(1):590-607. doi: 10.1080/17513758.2020.1788182.

A mathematical model for human-to-human transmission of COVID-19: a case study for Turkey's data.

Math Biosci Eng. 2021 Nov 5;18(6):9787-9805. doi: 10.3934/mbe.2021480.

SARS-CoV-2 and self-medication in Cameroon: a mathematical model.

J Biol Dyn. 2021 Dec;15(1):137-150. doi: 10.1080/17513758.2021.1883130.

Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19.

Front Public Health. 2021 Feb 12;9:593417. doi: 10.3389/fpubh.2021.593417. eCollection 2021.

A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics.

Math Biosci Eng. 2022 Jan;19(2):1213-1238. doi: 10.3934/mbe.2022056. Epub 2021 Dec 1.

SARS-CoV-2 Serial Interval Variation, Montana, USA, March 1-July 31, 2020.

Emerg Infect Dis. 2021 May;27(5):1486-1491. doi: 10.3201/eid2705.204663.

引用本文的文献

Operational stability study of lactate biosensors: modeling, parameter identification, and stability analysis.

Front Bioeng Biotechnol. 2024 Jul 16;12:1385459. doi: 10.3389/fbioe.2024.1385459. eCollection 2024.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

PLoS Comput Biol. 2024 May 17;20(5):e1012124. doi: 10.1371/journal.pcbi.1012124. eCollection 2024 May.

Epidemic models: why and how to use them.

Anaesth Crit Care Pain Med. 2022 Apr;41(2):101048. doi: 10.1016/j.accpm.2022.101048. Epub 2022 Feb 28.

本文引用的文献

Emerging dynamics from high-resolution spatial numerical epidemics.

Elife. 2021 Oct 15;10:e71417. doi: 10.7554/eLife.71417.

Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France.

Anaesth Crit Care Pain Med. 2021 Aug;40(4):100943. doi: 10.1016/j.accpm.2021.100943.

Memory is key in capturing COVID-19 epidemiological dynamics.

Epidemics. 2021 Jun;35:100459. doi: 10.1016/j.epidem.2021.100459. Epub 2021 Apr 27.

Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic.

PLoS Comput Biol. 2021 Mar 4;17(3):e1008776. doi: 10.1371/journal.pcbi.1008776. eCollection 2021 Mar.

Opinion: What models can and cannot tell us about COVID-19.

Proc Natl Acad Sci U S A. 2020 Jul 14;117(28):16092-16095. doi: 10.1073/pnas.2011542117. Epub 2020 Jun 24.

Estimating the burden of SARS-CoV-2 in France.

Science. 2020 Jul 10;369(6500):208-211. doi: 10.1126/science.abc3517. Epub 2020 May 13.

Special report: The simulations driving the world's response to COVID-19.

Nature. 2020 Apr;580(7803):316-318. doi: 10.1038/d41586-020-01003-6.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks.

Epidemics. 2019 Dec;29:100356. doi: 10.1016/j.epidem.2019.100356. Epub 2019 Aug 26.

Inferring R0 in emerging epidemics-the effect of common population structure is small.

J R Soc Interface. 2016 Aug;13(121). doi: 10.1098/rsif.2016.0288.