• 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

实时预测和预测 COVID-19 在土耳其的传播和医疗保健需求:一项建模研究。

Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study.

机构信息

Public Health Department, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey.

Center for Primary Care and Public Health, Queen Mary University of London, London, United Kingdom.

出版信息

Front Public Health. 2021 Jan 20;8:575145. doi: 10.3389/fpubh.2020.575145. eCollection 2020.

DOI:10.3389/fpubh.2020.575145
PMID:33553085
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7855976/
Abstract

This study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey. This study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted a SEIR-based simulator (TURKSAS) in four scenarios. According to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88.1%, and the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to be exceeded by 4.4-fold and non-ICU bed capacity by 3.21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey's capacity. Predictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modeling epidemics and establishing decision support systems is an important requirement.

摘要

本研究旨在估计受感染人数,评估 NPIs 对医疗保健系统的影响,并预测土耳其 COVID-19 的病例、死亡和住院人数。本研究从三个维度进行。在第一个维度,估算实际受感染人数。在第二个维度,在没有干预的情况下,预测预期的总感染人数、死亡人数和住院人数。在第三个维度,基于 SEIR 的模拟器(TURKSAS)预测了在四个场景下受感染人数和死亡人数以及 ICU 和非 ICU 床位需求的分布随时间的变化。根据死亡人数,土耳其在 3 月 21 日的估计受感染人数为 123030 人。在没有干预的情况下,预期感染人数为 72091595 人,死亡人数为 445956 人,发病率为 88.1%,死亡率为 0.54%。土耳其的 ICU 床位容量预计将增加 4.4 倍,非 ICU 床位容量将增加 3.21 倍。在第二和第三个场景中,遵守 NPIs 可以减少 94303 例预期死亡。在这两种情况下,预测的 ICU 和非 ICU 占用床位的峰值仍低于土耳其的容量。预测表明,通过全面遵守所采取的措施,可以预防约 1600 万人感染,预防 94000 人死亡。建立流行病模型和决策支持系统是一个重要要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/de7724122e5e/fpubh-08-575145-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/0ec6effee1ba/fpubh-08-575145-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/e06ac411b617/fpubh-08-575145-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/8a4f64a3fa8b/fpubh-08-575145-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/04a2f2ae55de/fpubh-08-575145-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/a05b32dead74/fpubh-08-575145-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/79f288102163/fpubh-08-575145-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/de7724122e5e/fpubh-08-575145-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/0ec6effee1ba/fpubh-08-575145-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/e06ac411b617/fpubh-08-575145-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/8a4f64a3fa8b/fpubh-08-575145-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/04a2f2ae55de/fpubh-08-575145-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/a05b32dead74/fpubh-08-575145-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/79f288102163/fpubh-08-575145-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0696/7855976/de7724122e5e/fpubh-08-575145-g0007.jpg

相似文献

1
Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study.实时预测和预测 COVID-19 在土耳其的传播和医疗保健需求:一项建模研究。
Front Public Health. 2021 Jan 20;8:575145. doi: 10.3389/fpubh.2020.575145. eCollection 2020.
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
How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.新墨西哥州如何利用新冠疫情预测模型来预先满足该州的医疗保健需求:定量分析
JMIR Public Health Surveill. 2021 Jun 9;7(6):e27888. doi: 10.2196/27888.
4
COVID-19 healthcare demand and mortality in Sweden in response to non-pharmaceutical mitigation and suppression scenarios.瑞典针对非药物缓解和抑制情景的 COVID-19 医疗需求和死亡率。
Int J Epidemiol. 2020 Oct 1;49(5):1443-1453. doi: 10.1093/ije/dyaa121.
5
Estimated surge in hospital and intensive care admission because of the coronavirus disease 2019 pandemic in the Greater Toronto Area, Canada: a mathematical modelling study.由于 2019 年冠状病毒病在加拿大大多伦多地区的流行,预计医院和重症监护病房的入院人数将会增加:一项数学建模研究。
CMAJ Open. 2020 Sep 22;8(3):E593-E604. doi: 10.9778/cmajo.20200093. Print 2020 Jul-Sep.
6
Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19.卡塔尔 2019 年冠状病毒病疫情的数学建模及其对卡塔尔 COVID-19 疫情应对措施的影响。
J Glob Health. 2021 Jan 16;11:05005. doi: 10.7189/jogh.11.05005.
7
[Bed capacity management in times of the COVID-19 pandemic : A simulation-based prognosis of normal and intensive care beds using the descriptive data of the University Hospital Augsburg].[新冠疫情期间的床位容量管理:利用奥格斯堡大学医院的描述性数据对普通病房和重症监护病房床位进行基于模拟的预测]
Anaesthesist. 2020 Oct;69(10):717-725. doi: 10.1007/s00101-020-00830-6. Epub 2020 Aug 21.
8
Effect of school closures on mortality from coronavirus disease 2019: old and new predictions.学校关闭对 2019 年冠状病毒病死亡率的影响:旧的和新的预测。
BMJ. 2020 Oct 7;371:m3588. doi: 10.1136/bmj.m3588.
9
Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.预测和预报 COVID-19 局部暴发的影响:使用 SEIR-D 定量流行病学模型预测医疗需求和能力。
Int J Epidemiol. 2021 Aug 30;50(4):1103-1113. doi: 10.1093/ije/dyab106.
10
Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies.预测英国的 COVID-19 动态:短期预测和潜在退出策略分析。
PLoS Comput Biol. 2021 Jan 22;17(1):e1008619. doi: 10.1371/journal.pcbi.1008619. eCollection 2021 Jan.

引用本文的文献

1
A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.一种使用原型分析预测新冠疫情医院床位占用情况的模拟模型。
Healthc Anal (N Y). 2023 Nov;3:100197. doi: 10.1016/j.health.2023.100197. Epub 2023 May 26.
2
Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes.威斯康星州 HERC 地区实时估计和预测 COVID-19 病例和住院情况,以支持公共卫生决策过程。
BMC Public Health. 2023 Feb 17;23(1):359. doi: 10.1186/s12889-023-15160-6.
3
Recommendations for developing clinical care protocols during pandemics: From theory and practice.

本文引用的文献

1
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.
2
Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China.有效的遏制解释了近期中国确诊 COVID-19 病例呈次指数级增长的原因。
Science. 2020 May 15;368(6492):742-746. doi: 10.1126/science.abb4557. Epub 2020 Apr 8.
3
Covid-19: four fifths of cases are asymptomatic, China figures indicate.
大流行期间制定临床护理方案的建议:从理论到实践。
Best Pract Res Clin Anaesthesiol. 2021 Oct;35(3):461-475. doi: 10.1016/j.bpa.2021.02.002. Epub 2021 Feb 27.
4
COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey.使用自回归积分移动平均 (ARIMA) 和人工神经网络 (ANN) 预测 COVID-19 流行率:土耳其案例。
J Infect Public Health. 2021 Jul;14(7):811-816. doi: 10.1016/j.jiph.2021.04.015. Epub 2021 May 5.
5
The Effectiveness of Community-based Social Distancing for Mitigating the Spread of the COVID-19 Pandemic in Turkey.基于社区的社交距离措施对减轻土耳其新冠疫情传播的有效性。
J Prev Med Public Health. 2020 Nov;53(6):397-404. doi: 10.3961/jpmph.20.381. Epub 2020 Nov 2.
中国数据显示,新冠疫情:五分之四的病例无症状。
BMJ. 2020 Apr 2;369:m1375. doi: 10.1136/bmj.m1375.
4
Estimates of the severity of coronavirus disease 2019: a model-based analysis.新型冠状病毒疾病 2019 严重程度的估计:基于模型的分析。
Lancet Infect Dis. 2020 Jun;20(6):669-677. doi: 10.1016/S1473-3099(20)30243-7. Epub 2020 Mar 30.
5
Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020.估算 2020 年日本横滨钻石公主号游轮上的 2019 年冠状病毒病(COVID-19)病例的无症状比例。
Euro Surveill. 2020 Mar;25(10). doi: 10.2807/1560-7917.ES.2020.25.10.2000180.
6
Real estimates of mortality following COVID-19 infection.新冠病毒感染后死亡率的实际估计值。
Lancet Infect Dis. 2020 Jul;20(7):773. doi: 10.1016/S1473-3099(20)30195-X. Epub 2020 Mar 12.
7
Unveiling the Origin and Transmission of 2019-nCoV.揭开 2019-nCoV 的起源与传播之谜。
Trends Microbiol. 2020 Apr;28(4):239-240. doi: 10.1016/j.tim.2020.02.001. Epub 2020 Feb 24.
8
The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application.新型冠状病毒肺炎(COVID-19)的潜伏期来自公开报告的确诊病例:估计和应用。
Ann Intern Med. 2020 May 5;172(9):577-582. doi: 10.7326/M20-0504. Epub 2020 Mar 10.
9
A Case Series of Children With 2019 Novel Coronavirus Infection: Clinical and Epidemiological Features.儿童 2019 年新型冠状病毒感染病例系列:临床和流行病学特征。
Clin Infect Dis. 2020 Sep 12;71(6):1547-1551. doi: 10.1093/cid/ciaa198.
10
Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study.中国武汉严重 COVID-19 患者的临床病程和结局:一项单中心、回顾性、观察性研究。
Lancet Respir Med. 2020 May;8(5):475-481. doi: 10.1016/S2213-2600(20)30079-5. Epub 2020 Feb 24.