• 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

非药物干预措施对沙特阿拉伯 COVID-19 疫情的影响。

Effect of non-pharmacological interventions on the COVID-19 epidemic in Saudi Arabia.

机构信息

Harvard T. H. Chan School of Public Health, Boston, MI, USA.

Consultant at King Faisal Specialist Hospital & Research Centre, Jeddah, Saudi Arabia.

出版信息

Epidemiol Infect. 2021 Nov 29;149:e252. doi: 10.1017/S0950268821002612.

DOI:10.1017/S0950268821002612
PMID:34839841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8692846/
Abstract

We quantified the potential impact of different social distancing and self-isolation scenarios on the coronavirus disease 2019 (COVID-19) pandemic trajectory in Saudi Arabia and compared the modelling results to the confirmed epidemic trajectory. Using the susceptible, exposed, infected, quarantined and self-isolated, requiring hospitalisation, recovered/immune individuals, fatalities model, we assessed the impact of a non-pharmacological interventions' subset. An unmitigated scenario (baseline), mitigation scenarios (25% reduction in social contact/twofold increase in self-isolation) and enhanced mitigation scenarios (50% reduction in social contact/twofold increase in self-isolation) were assessed and compared to the actual epidemic trajectory. For the unmitigated scenario, mitigation scenarios, enhanced mitigation scenarios and actual observed epidemic, the peak daily incidence rates (per 10 000 population) were 77.00, 16.00, 9.00 and 1.14 on days 71, 54, 35 and 136, respectively. The peak fatality rates were 35.00, 13.00, 5.00 and 0.016 on days 150, 125, 60 and 155, respectively. The R0 was 1.15, 1.14, 1.22 and 2.50, respectively. Aggressive implementation of social distancing and self-isolation contributed to the downward trend of the disease. We recommend using extensive models that comprehensively consider the natural history of COVID-19, social and behavioural patterns, age-specific data, actual network topology and population to elucidate the epidemic's magnitude and trajectory.

摘要

我们量化了不同社交距离和自我隔离场景对沙特阿拉伯 2019 年冠状病毒病(COVID-19)大流行轨迹的潜在影响,并将建模结果与确诊的流行轨迹进行了比较。使用易感者、暴露者、感染者、隔离者和自我隔离者、需要住院治疗者、康复/免疫者和死亡者模型,我们评估了一组非药物干预措施的影响。评估了未缓解情景(基线)、缓解情景(社交接触减少 25%,自我隔离增加一倍)和强化缓解情景(社交接触减少 50%,自我隔离增加一倍),并将其与实际流行轨迹进行了比较。对于未缓解情景、缓解情景、强化缓解情景和实际观察到的流行,每日最高发病率(每 10000 人)分别在第 71、54、35 和 136 天达到 77.00、16.00、9.00 和 1.14。每日最高死亡率分别在第 150、125、60 和 155 天达到 35.00、13.00、5.00 和 0.016。基本再生数分别为 1.15、1.14、1.22 和 2.50。积极实施社交距离和自我隔离有助于疾病的下降趋势。我们建议使用广泛的模型,全面考虑 COVID-19 的自然史、社会和行为模式、年龄特定数据、实际网络拓扑和人口,以阐明疫情的规模和轨迹。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/dcb4b7415615/S0950268821002612_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/8c41fe7a4100/S0950268821002612_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/f1a605c4a3ef/S0950268821002612_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/87144d95df75/S0950268821002612_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/6a9a4e692851/S0950268821002612_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/9b98c1afb01b/S0950268821002612_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/dcb4b7415615/S0950268821002612_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/8c41fe7a4100/S0950268821002612_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/f1a605c4a3ef/S0950268821002612_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/87144d95df75/S0950268821002612_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/6a9a4e692851/S0950268821002612_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/9b98c1afb01b/S0950268821002612_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e48e/8692846/dcb4b7415615/S0950268821002612_fig6.jpg

相似文献

1
Effect of non-pharmacological interventions on the COVID-19 epidemic in Saudi Arabia.非药物干预措施对沙特阿拉伯 COVID-19 疫情的影响。
Epidemiol Infect. 2021 Nov 29;149:e252. doi: 10.1017/S0950268821002612.
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
Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study.干预措施以减轻 SARS-CoV-2 在新加坡的早期传播:一项建模研究。
Lancet Infect Dis. 2020 Jun;20(6):678-688. doi: 10.1016/S1473-3099(20)30162-6. Epub 2020 Mar 23.
4
The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia.沙特阿拉伯控制 COVID-19 疫情的干预策略和预防措施的影响。
Math Biosci Eng. 2020 Nov 13;17(6):8123-8137. doi: 10.3934/mbe.2020412.
5
Determining the level of social distancing necessary to avoid future COVID-19 epidemic waves: a modelling study for North East London.确定避免未来 COVID-19 疫情波的必要社交距离水平:伦敦东北部的建模研究。
Sci Rep. 2021 Mar 11;11(1):5806. doi: 10.1038/s41598-021-84907-1.
6
COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis.新冠病毒在沙特阿拉伯的传播:建模、模拟与分析。
Int J Environ Res Public Health. 2020 Oct 23;17(21):7744. doi: 10.3390/ijerph17217744.
7
Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.利用 SIR 模型和机器学习在智慧医疗中测量和预防 COVID-19。
J Healthc Eng. 2020 Oct 29;2020:8857346. doi: 10.1155/2020/8857346. eCollection 2020.
8
The effect of control measures on COVID-19 transmission in South Korea.控制措施对韩国 COVID-19 传播的影响。
PLoS One. 2021 Mar 29;16(3):e0249262. doi: 10.1371/journal.pone.0249262. eCollection 2021.
9
A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing.一个具有明确易感染人群和无症状隔离舱的新型 COVID-19 传染病模型揭示了时机性社会隔离的意外后果。
J Theor Biol. 2021 Feb 7;510:110539. doi: 10.1016/j.jtbi.2020.110539. Epub 2020 Nov 24.
10
Epidemic Landscape and Forecasting of SARS-CoV-2 in India.印度 SARS-CoV-2 的疫情形势和预测。
J Epidemiol Glob Health. 2021 Mar;11(1):55-59. doi: 10.2991/jegh.k.200823.001. Epub 2020 Aug 28.

引用本文的文献

1
Theoretical Epidemiology Needs Urgent Attention in China.理论流行病学在中国亟需关注。
China CDC Wkly. 2024 May 24;6(21):499-502. doi: 10.46234/ccdcw2024.096.
2
Assessing the Impact of Non-Pharmaceutical Interventions on Consumer Mobility Patterns and COVID-19 Transmission in the US.评估非药物干预措施对美国消费者流动性模式和 COVID-19 传播的影响。
Int J Environ Res Public Health. 2024 Jan 7;21(1):67. doi: 10.3390/ijerph21010067.

本文引用的文献

1
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis.COVID-19大流行的数学模型:比较分析
J Indian Inst Sci. 2020;100(4):793-807. doi: 10.1007/s41745-020-00200-6. Epub 2020 Oct 30.
2
Public Trust and Compliance with the Precautionary Measures Against COVID-19 Employed by Authorities in Saudi Arabia.沙特阿拉伯当局采取的针对新冠疫情的预防措施的公众信任度与遵守情况。
Risk Manag Healthc Policy. 2020 Jul 8;13:753-760. doi: 10.2147/RMHP.S257287. eCollection 2020.
3
Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries.
物理隔离干预措施与 2019 年冠状病毒病发病率:149 个国家的自然实验。
BMJ. 2020 Jul 15;370:m2743. doi: 10.1136/bmj.m2743.
4
Importance of early precautionary actions in avoiding the spread of COVID-19: Saudi Arabia as an Example.早期预防措施在避免新冠病毒传播中的重要性:以沙特阿拉伯为例。
Saudi Pharm J. 2020 Jul;28(7):898-902. doi: 10.1016/j.jsps.2020.05.005. Epub 2020 May 22.
5
Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.非药物干预对英国 COVID-19 病例、死亡和医院服务需求的影响:一项建模研究。
Lancet Public Health. 2020 Jul;5(7):e375-e385. doi: 10.1016/S2468-2667(20)30133-X. Epub 2020 Jun 2.
6
Clinical testing for COVID-19.COVID-19 的临床检测。
J Allergy Clin Immunol. 2020 Jul;146(1):23-34. doi: 10.1016/j.jaci.2020.05.012. Epub 2020 May 20.
7
Pathways to COVID-19 'community protection'.通往 COVID-19“社区保护”的途径。
Int J Infect Dis. 2020 Jul;96:496-499. doi: 10.1016/j.ijid.2020.05.058. Epub 2020 May 18.
8
Modeling COVID-19 and Its Impacts on U.S. Immigration and Customs Enforcement (ICE) Detention Facilities, 2020.建模 COVID-19 及其对美国移民与海关执法(ICE)拘留设施的影响,2020 年。
J Urban Health. 2020 Aug;97(4):439-447. doi: 10.1007/s11524-020-00441-x.
9
Effect of non-pharmaceutical interventions to contain COVID-19 in China.中国采取的非药物性干预措施对遏制 2019 冠状病毒病的效果。
Nature. 2020 Sep;585(7825):410-413. doi: 10.1038/s41586-020-2293-x. Epub 2020 May 4.
10
Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus.数学评估非药物干预措施对遏制 2019 年新型冠状病毒的影响。
Math Biosci. 2020 Jul;325:108364. doi: 10.1016/j.mbs.2020.108364. Epub 2020 May 1.