• 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

中国南京 5 种 SARS-CoV-2 株引起本土聚集性疫情传播动力学的参数分析。

Parametric analysis of the transmission dynamics during indigenous aggregated outbreaks caused by five SARS-CoV-2 strains in Nanjing, China.

机构信息

Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.

Wujin District Center for Disease Control and Prevention, Changzhou, China.

出版信息

Front Public Health. 2024 Mar 8;12:1358577. doi: 10.3389/fpubh.2024.1358577. eCollection 2024.

DOI:10.3389/fpubh.2024.1358577
PMID:38525336
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10959284/
Abstract

BACKGROUND

SARS-CoV-2 strains have been of great concern due to their high infectivity and antibody evasion.

METHODS

In this study, data were collected on indigenous aggregated outbreaks in Nanjing from January 2020 to December 2022, caused by five strains including the original strain, the Delta variant, and the Omicron variant (BA.2, BA.5.2, and BF.7). The basic epidemiological characteristics of infected individuals were described and then parametric analysis of transmission dynamics was performed, including the calculation of incubation period, serial interval (SI), the basic reproductive number (R), and the household secondary attack rate (HSAR). Finally, we compared the trends of transmission dynamic parameters of different strains.

RESULTS

The incubation period for the original strain, the Delta variant, Omicron BA.2, Omicron BA.5.2, and Omicron BF.7 were 6 d (95% CI: 3.5-7.5 d), 5 d (95% CI: 4.0-6.0 d), 3 d (95% CI: 3.0-4.0 d), 3 d (95% CI: 3.0-3.0 d), and 2 d (95% CI: 2.0-3.0 d), respectively; Also, the SI of the five strains were 5.69 d, 4.79 d, 2.7 d, 2.12 d, and 2.43 d, respectively. Notably, the incubation period and SI of the five had both a progressive shortening trend ( < 0.001); Moreover, R of the five were 2.39 (95% CI: 1.30-4.29), 3.73 (95% CI: 2.66-5.15), 5.28 (95% CI: 3.52-8.10), 5.54 (95% CI: 2.69-11.17), 7.39 (95% CI: 2.97-18.76), with an increasing trend gradually ( < 0.01); HSAR of the five were 25.5% (95% CI: 20.1-31.7%), 27.4% (95% CI: 22.0-33.4%), 42.9% (95% CI: 34.3-51.8%), 53.1% (95% CI: 45.0-60.9%), 41.4% (95% CI, 25.5-59.3%), also with an increasing trend ( < 0.001).

CONCLUSION

Compared to the original strain, the incubation period and SI decreased while R and HSAR increased, suggesting that transmission in the population was faster and the scope of the population was wider. Overall, it's crucial to keep implementing comprehensive measures like monitoring and alert systems, herd immunization plans, and outbreak control.

摘要

背景

由于 SARS-CoV-2 变异株具有高传染性和抗体逃逸能力,因此一直备受关注。

方法

本研究收集了 2020 年 1 月至 2022 年 12 月期间南京发生的 5 种毒株(包括原始株、Delta 变异株和奥密克戎变异株(BA.2、BA.5.2 和 BF.7)引起的本地聚集性疫情数据。描述了感染者的基本流行病学特征,然后对传播动力学进行参数分析,包括潜伏期、序列间隔(SI)、基本繁殖数(R)和家庭二次攻击率(HSAR)的计算。最后,我们比较了不同毒株传播动态参数的趋势。

结果

原始株、Delta 变异株、奥密克戎 BA.2、奥密克戎 BA.5.2 和奥密克戎 BF.7 的潜伏期分别为 6d(95%CI:3.5-7.5d)、5d(95%CI:4.0-6.0d)、3d(95%CI:3.0-4.0d)、3d(95%CI:3.0-3.0d)和 2d(95%CI:2.0-3.0d);此外,这 5 种毒株的 SI 分别为 5.69d、4.79d、2.7d、2.12d 和 2.43d。值得注意的是,这 5 种毒株的潜伏期和 SI 均呈渐进缩短趋势( < 0.001);此外,这 5 种毒株的 R 值分别为 2.39(95%CI:1.30-4.29)、3.73(95%CI:2.66-5.15)、5.28(95%CI:3.52-8.10)、5.54(95%CI:2.69-11.17)和 7.39(95%CI:2.97-18.76),呈逐渐增加趋势( < 0.01);这 5 种毒株的 HSAR 分别为 25.5%(95%CI:20.1-31.7%)、27.4%(95%CI:22.0-33.4%)、42.9%(95%CI:34.3-51.8%)、53.1%(95%CI:45.0-60.9%)和 41.4%(95%CI,25.5-59.3%),也呈上升趋势( < 0.001)。

结论

与原始株相比,潜伏期和 SI 缩短,而 R 和 HSAR 增加,提示人群中的传播速度更快,人群范围更广。总的来说,必须继续实施综合措施,如监测和警报系统、群体免疫计划和疫情控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/4f2112aeca95/fpubh-12-1358577-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/330cac2c2e74/fpubh-12-1358577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/edbaf9f69f0b/fpubh-12-1358577-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/b8ff07df3246/fpubh-12-1358577-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/4f2112aeca95/fpubh-12-1358577-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/330cac2c2e74/fpubh-12-1358577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/edbaf9f69f0b/fpubh-12-1358577-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/b8ff07df3246/fpubh-12-1358577-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/767f/10959284/4f2112aeca95/fpubh-12-1358577-g004.jpg

相似文献

1
Parametric analysis of the transmission dynamics during indigenous aggregated outbreaks caused by five SARS-CoV-2 strains in Nanjing, China.中国南京 5 种 SARS-CoV-2 株引起本土聚集性疫情传播动力学的参数分析。
Front Public Health. 2024 Mar 8;12:1358577. doi: 10.3389/fpubh.2024.1358577. eCollection 2024.
2
[Epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, China].[中国广州2019-nCoV奥密克戎变异株引起的两起本地新冠肺炎疫情的流行病学特征]
Zhonghua Liu Xing Bing Xue Za Zhi. 2022 Nov 10;43(11):1705-1710. doi: 10.3760/cma.j.cn112338-20220523-00450.
3
Comparison of epidemiological characteristics and transmissibility of different strains of COVID-19 based on the incidence data of all local outbreaks in China as of March 1, 2022.基于截至 2022 年 3 月 1 日中国所有本地疫情的发病数据,比较不同 COVID-19 株的流行病学特征和传播性。
Front Public Health. 2022 Sep 15;10:949594. doi: 10.3389/fpubh.2022.949594. eCollection 2022.
4
Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study.中国上海新型冠状病毒奥密克戎变异株引发疫情的流行病学特征及传播动力学:一项描述性研究
medRxiv. 2022 Jun 18:2022.06.11.22276273. doi: 10.1101/2022.06.11.22276273.
5
Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis.评估关注的 SARS-CoV-2 变异株的潜伏期、序列间隔和代时变化:系统评价和荟萃分析。
BMC Med. 2023 Sep 29;21(1):374. doi: 10.1186/s12916-023-03070-8.
6
Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants.SARS-CoV-2 德尔塔和奥密克戎变异株的连续间隔快速审查和荟萃分析。
BMC Infect Dis. 2023 Jun 26;23(1):429. doi: 10.1186/s12879-023-08407-5.
7
Transmission characteristics and inactivated vaccine effectiveness against transmission of the SARS-CoV-2 Omicron BA.2 variant in Shenzhen, China.中国深圳 SARS-CoV-2 奥密克戎 BA.2 变异株传播特性和灭活疫苗对传播的有效性。
Front Immunol. 2024 Jan 8;14:1290279. doi: 10.3389/fimmu.2023.1290279. eCollection 2023.
8
Transmission dynamics of SARS-CoV-2 Omicron variant infections in Hangzhou, Zhejiang, China, January-February 2022.2022 年 1 月至 2 月在中国浙江杭州发生的 SARS-CoV-2 奥密克戎变异株感染的传播动力学。
Int J Infect Dis. 2023 Jan;126:132-135. doi: 10.1016/j.ijid.2022.10.033. Epub 2022 Oct 28.
9
New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach.利用输入病例数据进行新型 SARS-CoV-2 变异株社区获得性暴发预警的新监测指标:贝叶斯马尔可夫链蒙特卡罗方法。
JMIR Public Health Surveill. 2022 Nov 25;8(11):e40866. doi: 10.2196/40866.
10
Effectiveness of COVID-19 vaccines against SARS-CoV-2 Omicron variants during two outbreaks from March to May 2022 in Quzhou, China.中国衢州 2022 年 3 月至 5 月两次疫情期间 COVID-19 疫苗对 SARS-CoV-2 奥密克戎变异株的有效性。
Hum Vaccin Immunother. 2023 Dec 31;19(1):2163813. doi: 10.1080/21645515.2022.2163813. Epub 2023 Jan 27.

引用本文的文献

1
China's policies: post-COVID-19 challenges for the older population.中国的政策:后新冠疫情时代对老年人口的挑战。
Glob Health Action. 2024 Dec 31;17(1):2345968. doi: 10.1080/16549716.2024.2345968. Epub 2024 May 8.

本文引用的文献

1
Subunit vaccine raised against the SARS-CoV-2 spike of Delta and Omicron variants.针对 Delta 和奥密克戎变异株的 SARS-CoV-2 刺突蛋白的亚单位疫苗。
J Med Virol. 2023 Oct;95(10):e29160. doi: 10.1002/jmv.29160.
2
BF.7: a new Omicron subvariant characterized by rapid transmission.BF.7:一种以快速传播为特征的新型奥密克戎亚变种。
Clin Microbiol Infect. 2024 Jan;30(1):137-141. doi: 10.1016/j.cmi.2023.09.018. Epub 2023 Oct 5.
3
Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis.
中国大陆 COVID-19 大流行的流行病学特征和动态传播:轨迹聚类分析视角。
Epidemics. 2023 Dec;45:100719. doi: 10.1016/j.epidem.2023.100719. Epub 2023 Sep 26.
4
Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis.评估关注的 SARS-CoV-2 变异株的潜伏期、序列间隔和代时变化:系统评价和荟萃分析。
BMC Med. 2023 Sep 29;21(1):374. doi: 10.1186/s12916-023-03070-8.
5
Mathematical Modelling of Virus Spreading in COVID-19.新冠病毒传播的数学建模
Viruses. 2023 Aug 23;15(9):1788. doi: 10.3390/v15091788.
6
[Comparison of incubation periods of infections of Omicron variants BA.2 and BF.7 in Beijing].[北京奥密克戎变异株BA.2和BF.7感染潜伏期的比较]
Zhonghua Liu Xing Bing Xue Za Zhi. 2023 Sep 10;44(9):1397-1401. doi: 10.3760/cma.j.cn112338-20230316-00153.
7
Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis.感染 SARS-CoV-2 后患长新冠主要症状的风险:系统评价和荟萃分析。
Sci Rep. 2023 Sep 15;13(1):15332. doi: 10.1038/s41598-023-42321-9.
8
Transmission dynamics informed neural network with application to COVID-19 infections.基于传播动力学的神经网络及其在 COVID-19 感染中的应用。
Comput Biol Med. 2023 Oct;165:107431. doi: 10.1016/j.compbiomed.2023.107431. Epub 2023 Sep 1.
9
Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.从时间聚合的发病数据估计传染病的基本再生数:一种统计建模方法和软件工具。
PLoS Comput Biol. 2023 Aug 28;19(8):e1011439. doi: 10.1371/journal.pcbi.1011439. eCollection 2023 Aug.
10
Improved time-varying reproduction numbers using the generation interval for COVID-19.利用 COVID-19 的代际间隔改进时变繁殖数。
Front Public Health. 2023 Jun 30;11:1185854. doi: 10.3389/fpubh.2023.1185854. eCollection 2023.