• 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

描述巴西圣保罗地区的 CoViD-19 的数学模型——评估隔离作为控制机制以及预测疫情释放的流行病学情景。

Mathematical model describing CoViD-19 in São Paulo, Brazil - evaluating isolation as control mechanism and forecasting epidemiological scenarios of release.

机构信息

Department of Applied Mathematics, State University of Campinas, General Hospital of the Medicine School of University of São Paulo, Campinas, Brazil.

Division of Allergy and Immunology, General Hospital of the Medicine School of University of São Paulo, Campinas, Brazil.

出版信息

Epidemiol Infect. 2020 Jul 20;148:e155. doi: 10.1017/S0950268820001600.

DOI:10.1017/S0950268820001600
PMID:32684175
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7378372/
Abstract

In São Paulo, Brazil, the first case of coronavirus disease 2019 (CoViD-19) was confirmed on 26 February, the first death due to CoViD-19 was registered on 16 March, and on 24 March, São Paulo implemented the isolation of persons in non-essential activities. A mathematical model was formulated based on non-linear ordinary differential equations considering young (60 years old or less) and elder (60 years old or more) subpopulations, aiming to describe the introduction and dissemination of the new coronavirus in São Paulo. This deterministic model used the data collected from São Paulo to estimate the model parameters, obtaining R0 = 6.8 for the basic reproduction number. The model also allowed to estimate that 50% of the population of São Paulo was in isolation, which permitted to describe the current epidemiological status. The goal of isolation implemented in São Paulo to control the rapid increase of the new coronavirus epidemic was partially succeeded, concluding that if isolation of at least 80% of the population had been implemented, the collapse in the health care system could be avoided. Nevertheless, the isolated persons must be released one day. Based on this model, we studied the potential epidemiological scenarios of release by varying the proportions of the release of young and elder persons. We also evaluated three different strategies of release: All isolated persons are released simultaneously, two and three releases divided in equal proportions. The better scenarios occurred when young persons are released, but maintaining elder persons isolated for a while. When compared with the epidemic without isolation, all strategies of release did not attain the goal of reducing substantially the number of hospitalisations due to severe CoViD-19. Hence, we concluded that the best decision must be postponing the beginning of the release.

摘要

在巴西圣保罗,2019 年冠状病毒病(CoViD-19)的首例病例于 2 月 26 日确诊,首例 CoViD-19 死亡病例于 3 月 16 日登记,3 月 24 日,圣保罗开始实施非必要活动人员隔离。本研究建立了一个基于考虑年轻(60 岁或以下)和老年(60 岁或以上)亚人群的非线性常微分方程的数学模型,旨在描述新型冠状病毒在圣保罗的引入和传播。该确定性模型使用从圣保罗收集的数据来估计模型参数,得到基本繁殖数 R0 = 6.8。该模型还估计了圣保罗 50%的人口处于隔离状态,这允许描述当前的流行病学状况。为控制新型冠状病毒疫情的快速蔓延而在圣保罗实施的隔离目标部分取得了成功,得出的结论是,如果至少 80%的人口实施隔离,就可以避免医疗保健系统崩溃。然而,隔离人员终有一天会被释放。基于这个模型,我们通过改变年轻和老年人口释放比例来研究释放的潜在流行病学情景。我们还评估了三种不同的释放策略:所有隔离人员同时释放、两批和三批等比例释放。当年轻人群释放时,情况会更好,但同时老年人群也需要隔离一段时间。与无隔离的疫情相比,所有释放策略都没有达到大幅减少因严重 CoViD-19 住院人数的目标。因此,我们得出的结论是,推迟释放的开始是最好的决定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/5ebdca22abdf/S0950268820001600_fig15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/47c20f3edb7f/S0950268820001600_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/bf944cbf1a36/S0950268820001600_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/693de51bedc9/S0950268820001600_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/60b6c19df0b7/S0950268820001600_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/4864cbf6b843/S0950268820001600_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/7345af84652e/S0950268820001600_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/5b62d3fbc16d/S0950268820001600_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/e6f4b021755e/S0950268820001600_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/e479ecfa24d0/S0950268820001600_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/550d07bd3873/S0950268820001600_fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/6eac1ef08123/S0950268820001600_fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/30dda563e45b/S0950268820001600_fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/75b405f34750/S0950268820001600_fig13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/a89b59435adf/S0950268820001600_fig14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/5ebdca22abdf/S0950268820001600_fig15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/47c20f3edb7f/S0950268820001600_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/bf944cbf1a36/S0950268820001600_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/693de51bedc9/S0950268820001600_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/60b6c19df0b7/S0950268820001600_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/4864cbf6b843/S0950268820001600_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/7345af84652e/S0950268820001600_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/5b62d3fbc16d/S0950268820001600_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/e6f4b021755e/S0950268820001600_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/e479ecfa24d0/S0950268820001600_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/550d07bd3873/S0950268820001600_fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/6eac1ef08123/S0950268820001600_fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/30dda563e45b/S0950268820001600_fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/75b405f34750/S0950268820001600_fig13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/a89b59435adf/S0950268820001600_fig14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84da/7378372/5ebdca22abdf/S0950268820001600_fig15.jpg

相似文献

1
Mathematical model describing CoViD-19 in São Paulo, Brazil - evaluating isolation as control mechanism and forecasting epidemiological scenarios of release.描述巴西圣保罗地区的 CoViD-19 的数学模型——评估隔离作为控制机制以及预测疫情释放的流行病学情景。
Epidemiol Infect. 2020 Jul 20;148:e155. doi: 10.1017/S0950268820001600.
2
Mathematical modeling of the transmission of SARS-CoV-2-Evaluating the impact of isolation in São Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of CoViD-19.SARS-CoV-2 传播的数学建模-评估与保护措施相关的巴西圣保罗州隔离和西班牙封锁对 CoViD-19 疫情的影响。
PLoS One. 2021 Jun 15;16(6):e0252271. doi: 10.1371/journal.pone.0252271. eCollection 2021.
3
Modeling the transmission of the new coronavirus in São Paulo State, Brazil-assessing the epidemiological impacts of isolating young and elder persons.建模巴西圣保罗州新型冠状病毒的传播——评估隔离年轻人和老年人的流行病学影响。
Math Med Biol. 2021 Jun 15;38(2):137-177. doi: 10.1093/imammb/dqaa015.
4
The COVID-19 pandemic in Brazil: chronicle of a health crisis foretold.巴西的新冠疫情:一场早有预兆的健康危机纪事。
Cad Saude Publica. 2020;36(5):e00068820. doi: 10.1590/0102-311x00068820. Epub 2020 May 8.
5
Predicting COVID-19 spread in the face of control measures in West Africa.预测西非控制措施下的 COVID-19 传播。
Math Biosci. 2020 Oct;328:108431. doi: 10.1016/j.mbs.2020.108431. Epub 2020 Jul 29.
6
Transmission patterns of COVID-19 in the mainland of China and the efficacy of different control strategies: a data- and model-driven study.中国大陆 COVID-19 的传播模式及不同防控策略的效果:基于数据和模型的研究。
Infect Dis Poverty. 2020 Jul 6;9(1):83. doi: 10.1186/s40249-020-00709-z.
7
Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study.隔离、检测、接触者追踪和保持社交距离在不同环境下减少 SARS-CoV-2 传播的效果:一项数学建模研究。
Lancet Infect Dis. 2020 Oct;20(10):1151-1160. doi: 10.1016/S1473-3099(20)30457-6. Epub 2020 Jun 16.
8
Epidemiological Investigation of the First 135 COVID-19 Cases in Brunei: Implications for Surveillance, Control, and Travel Restrictions.文莱首例 135 例 COVID-19 病例的流行病学调查:对监测、控制和旅行限制的启示。
Am J Trop Med Hyg. 2020 Oct;103(4):1608-1613. doi: 10.4269/ajtmh.20-0771.
9
Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia.建模马来西亚控制措施对预防 COVID-19 传播的有效性。
Int J Environ Res Public Health. 2020 Jul 30;17(15):5509. doi: 10.3390/ijerph17155509.
10
Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation.用于探索 COVID-19 干预和控制策略的灵活、免费的随机个体接触模型:开发和模拟。
JMIR Public Health Surveill. 2020 Sep 18;6(3):e18965. doi: 10.2196/18965.

引用本文的文献

1
Masks and respirators for prevention of respiratory infections: a state of the science review.口罩和呼吸防护器预防呼吸道感染:科学综述。
Clin Microbiol Rev. 2024 Jun 13;37(2):e0012423. doi: 10.1128/cmr.00124-23. Epub 2024 May 22.
2
Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020.局部保护气泡:对 2020 年 7 月巴西圣保罗市冠状病毒传播速度放缓的一种解释。
Cad Saude Publica. 2023 Dec 15;39(11):e00109522. doi: 10.1590/0102-311XEN109522. eCollection 2023.
3
A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.

本文引用的文献

1
Modeling the transmission of the new coronavirus in São Paulo State, Brazil-assessing the epidemiological impacts of isolating young and elder persons.建模巴西圣保罗州新型冠状病毒的传播——评估隔离年轻人和老年人的流行病学影响。
Math Med Biol. 2021 Jun 15;38(2):137-177. doi: 10.1093/imammb/dqaa015.
2
Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals.SARS-CoV-2 的空气动力学分析在两家武汉医院进行。
Nature. 2020 Jun;582(7813):557-560. doi: 10.1038/s41586-020-2271-3. Epub 2020 Apr 27.
3
Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand".
一种用于评估移动网络指标与新冠疫情之间关系的社会网络分析方法。
Health Serv Insights. 2023 May 17;16:11786329231173816. doi: 10.1177/11786329231173816. eCollection 2023.
4
Thresholds, bifurcation and chaos in biological phenomena: Comment on "Mathematical models for Dengue fever epidemiology: A 10-year systematic review" by M. Aguiar et al.生物现象中的阈值、分岔与混沌:评M. 阿吉亚尔等人的《登革热流行病学数学模型:十年系统综述》
Phys Life Rev. 2023 Mar;44:6-8. doi: 10.1016/j.plrev.2022.11.005. Epub 2022 Nov 24.
5
Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making.使用自组织映射神经网络和K均值方法对新冠肺炎病例进行基于聚类的分析,以改善医疗决策。
Inform Med Unlocked. 2022;32:101005. doi: 10.1016/j.imu.2022.101005. Epub 2022 Jul 5.
6
Mathematical modeling of the transmission of SARS-CoV-2-Evaluating the impact of isolation in São Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of CoViD-19.SARS-CoV-2 传播的数学建模-评估与保护措施相关的巴西圣保罗州隔离和西班牙封锁对 CoViD-19 疫情的影响。
PLoS One. 2021 Jun 15;16(6):e0252271. doi: 10.1371/journal.pone.0252271. eCollection 2021.
7
Spatiotemporal ecological study of COVID-19 mortality in the city of São Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions.巴西圣保罗市 COVID-19 死亡率的时空生态研究:高死亡率风险从社会经济条件最好的地区转移到最差的地区。
Travel Med Infect Dis. 2021 Jan-Feb;39:101945. doi: 10.1016/j.tmaid.2020.101945. Epub 2020 Dec 2.
8
Social network analysis of COVID-19 transmission in Karnataka, India.印度卡纳塔克邦 COVID-19 传播的社会网络分析。
Epidemiol Infect. 2020 Sep 25;148:e230. doi: 10.1017/S095026882000223X.
9
Diffusive process under Lifshitz scaling and pandemic scenarios.利夫希茨标度和大流行情景下的扩散过程。
Physica A. 2020 Dec 1;559:125092. doi: 10.1016/j.physa.2020.125092. Epub 2020 Aug 20.
评 Ferguson 等人的“减少 COVID-19 死亡率和医疗需求的非药物干预(NPIs)的影响”一文。
Bull Math Biol. 2020 Apr 8;82(4):52. doi: 10.1007/s11538-020-00726-x.