Suppr超能文献

2016-2019 年也门内战期间霍乱疫情特征。

Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019.

机构信息

Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.

Department of Neuroscience, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA.

出版信息

Int J Environ Res Public Health. 2021 Dec 30;19(1):378. doi: 10.3390/ijerph19010378.

Abstract

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov-Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana'a, Sana'a City, and Al-Hudaydah-the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.

摘要

全球霍乱控制工作队(GTFCC)制定了一项策略,以应对也门霍乱疫情,该策略旨在早期发现疫情、识别热点地区以及协调资源动员。该策略需要系统的方法来定义和分类疫情特征,即疫情曲线的特征及其特征。我们使用公开数据来量化也门当前霍乱疫情的爆发特征,并使用自适应时间序列方法对各省进行聚类。我们使用世界卫生组织(WHO)报告的 2016 年 9 月 4 日至 2019 年 12 月 29 日期间 20 个也门省和全国每周霍乱发病率的时间序列,对疫情特征和聚类进行了特征描述和识别。我们使用 Kolmogorov-Zurbenko 自适应滤波方法量化了关键点和时间段。根据关键点、关键时期和峰值幅度的相似性,我们将各省分配到具有相似爆发特征的六个聚类中。我们确定了从 2016 年 9 月 12 日、2017 年 3 月 6 日、2018 年 5 月 28 日和 2019 年 1 月 28 日开始的四个全国性疫情波次。在六个确定的聚类中,我们将萨那、萨那市和荷台达的核心区域热点地区归类为疫情的起源地。其他五个聚类在波 2 和波 3 的峰值频率、时间、幅度和地理位置上存在差异。截至 2019 年 12 月 29 日,没有任何一个省份恢复到波 1 之前的水平。爆发特征的相似性表明可能存在共同的环境和人为感染驱动因素;爆发特征的异质性意味着可能存在从核心区域热点向外传播的传播波,这些传播波可能受到值得进一步调查的因素的影响。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验