• 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

新冠疫情：意大利感染人群的一种自动半参数估计方法

CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy.

作者信息

Fenga Livio

机构信息

ISTAT, Rome, Italy.

出版信息

PeerJ. 2021 Mar 4;9:e10819. doi: 10.7717/peerj.10819. eCollection 2021.

DOI:10.7717/peerj.10819
PMID:33717677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7937344/
Abstract

To date, official data on the number of people infected with the SARS-CoV-2-responsible for the Covid-19-have been released by the Italian Government just on the basis of a non-representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this article proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected with the SARS-CoV-2 could be as high as 105.789.

摘要

迄今为止，意大利政府仅根据拭子检测呈阳性的非代表性人口样本公布了感染导致新冠疫情的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的人数官方数据。然而，对包括无症状感染者在内的感染人数进行可靠估计，对于制定运营方案以及估计未来在不同程度上需要医疗护理的人数至关重要。为了克服当前的数据缺陷，本文提出了一种基于自助法的严重急性呼吸综合征冠状病毒2感染人数估计程序。该方法旨在稳健、自动且适合在区域层面生成估计值。所得结果表明，虽然3月12日的官方数据报告意大利有12839例病例，但感染严重急性呼吸综合征冠状病毒2的人数可能高达105789例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/464da512c24b/peerj-09-10819-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/8299e51af9e3/peerj-09-10819-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/8d67507a5905/peerj-09-10819-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/7b89ea29bdc1/peerj-09-10819-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/3b7e09b68b5e/peerj-09-10819-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/bd85993404d6/peerj-09-10819-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/464da512c24b/peerj-09-10819-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/8299e51af9e3/peerj-09-10819-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/8d67507a5905/peerj-09-10819-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/7b89ea29bdc1/peerj-09-10819-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/3b7e09b68b5e/peerj-09-10819-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/bd85993404d6/peerj-09-10819-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c06e/7937344/464da512c24b/peerj-09-10819-g006.jpg

相似文献

1
CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy.新冠疫情：意大利感染人群的一种自动半参数估计方法
PeerJ. 2021 Mar 4;9:e10819. doi: 10.7717/peerj.10819. eCollection 2021.
2
Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by the End of March in Northern Italy and First Week of April in Southern Italy.意大利各地区的新冠疫情进展：北部地区将于 3 月底达到高峰，南部地区将于 4 月初达到高峰。
Int J Environ Res Public Health. 2020 Apr 27;17(9):3025. doi: 10.3390/ijerph17093025.
3
SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence.SARS-CoV-2 感染和 COVID-19 死亡率：感染死亡率和当前流行率的估计。
Int J Environ Res Public Health. 2020 Dec 11;17(24):9290. doi: 10.3390/ijerph17249290.
4
Prevalence of asymptomatic SARS-CoV-2-positive individuals in the general population of northern Italy and evaluation of a diagnostic serological ELISA test: a cross-sectional study protocol.意大利北部普通人群中无症状 SARS-CoV-2 阳性个体的流行情况及一种诊断用酶联免疫吸附试验 ELISA 检测的评估：一项横断面研究方案。
BMJ Open. 2020 Oct 6;10(10):e040036. doi: 10.1136/bmjopen-2020-040036.
5
[Seroprevalence of anti-SARS-CoV-2 IgG/IgM antibodies in Borgosesia (Piedmont Region, Northern Italy) population: a surveillance strategy in post-lockdown period?].[意大利北部皮埃蒙特大区博尔戈塞西亚人群中抗SARS-CoV-2 IgG/IgM抗体的血清流行率：封锁后时期的监测策略？]
Epidemiol Prev. 2020 Sep-Dec;44(5-6 Suppl 2):200-206. doi: 10.19191/EP20.5-6.S2.119.
6
Observed and estimated prevalence of Covid-19 in Italy: How to estimate the total cases from medical swabs data.意大利观察到和估计的新冠病毒流行率：如何从医疗拭子数据估计总病例数。
Sci Total Environ. 2021 Apr 10;764:142799. doi: 10.1016/j.scitotenv.2020.142799. Epub 2020 Oct 8.
7
COVID-19 Trend Estimation in the Elderly Italian Region of Sardinia.COVID-19 趋势在意大利撒丁岛老年人群中的估计。
Front Public Health. 2020 Apr 24;8:153. doi: 10.3389/fpubh.2020.00153. eCollection 2020.
8
Inappropriate risk perception for SARS-CoV-2 infection among Italian HCWs in the eve of COVID-19 pandemic.在新冠疫情前夕，意大利医护人员对新冠病毒感染存在不当的风险认知。
Acta Biomed. 2020 May 14;91(3):e2020040. doi: 10.23750/abm.v91i3.9727.
9
No Evidence of SARS-CoV-2 Circulation in Rome (Italy) during the Pre-Pandemic Period: Results of a Retrospective Surveillance.没有证据表明 SARS-CoV-2 在大流行前的罗马（意大利）传播：回顾性监测的结果。
Int J Environ Res Public Health. 2020 Nov 16;17(22):8461. doi: 10.3390/ijerph17228461.
10
Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region.新冠肺炎疫情重启策略的影响：皮埃蒙特大区的建模研究。
BMC Infect Dis. 2020 Oct 28;20(1):798. doi: 10.1186/s12879-020-05490-w.

引用本文的文献

1
Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features.宿主遗传学与新冠病毒疾病严重程度：利用布尔量子特征提高最新严重程度评分的准确性。
Front Genet. 2024 May 22;15:1362469. doi: 10.3389/fgene.2024.1362469. eCollection 2024.
2
Predictive Capacity of COVID-19 Test Positivity Rate.新冠病毒检测阳性率的预测能力。
Sensors (Basel). 2021 Apr 1;21(7):2435. doi: 10.3390/s21072435.
3
Containment effort reduction and regrowth patterns of the Covid-19 spreading.新冠疫情传播的防控措施放松及疫情复燃模式

本文引用的文献

1
Incidence, prevalence, and evidence. Scientific problems in epidemiologic statistics for the occurrence of cancer.
Am J Med. 1987 Jan;82(1):113-23. doi: 10.1016/0002-9343(87)90386-x.
Infect Dis Model. 2021 Apr 7;6:632-642. doi: 10.1016/j.idm.2021.02.003. eCollection 2021.
4
Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model.意大利新冠病毒（Sars-Cov-2）的演变分析、无症状感染者的作用以及逻辑模型的成效
Chaos Solitons Fractals. 2020 Nov;140:110150. doi: 10.1016/j.chaos.2020.110150. Epub 2020 Jul 28.
5
Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world.意大利新冠病毒传播趋势建模及全球疫情的通用预测。
Chaos Solitons Fractals. 2020 Oct;139:110064. doi: 10.1016/j.chaos.2020.110064. Epub 2020 Jul 1.
6
Predicting the evolution of the COVID-19 epidemic with the A-SIR model: Lombardy, Italy and São Paulo state, Brazil.使用A-SIR模型预测COVID-19疫情的演变：意大利伦巴第大区和巴西圣保罗州。
Physica D. 2020 Dec;413:132693. doi: 10.1016/j.physd.2020.132693. Epub 2020 Aug 12.