Social Network Analysis of Ebola Virus Disease During the 2014 Outbreak in Sukudu, Sierra Leone

Author:

Hazel Ashley1ORCID,Davidson Michelle C2,Rogers Abu3,Barrie M Bailor45,Freeman Adams5,Mbayoh Mohamed5,Kamara Mohamed5,Blumberg Seth1,Lietman Thomas M1,Rutherford George W46,Jones James Holland7,Porco Travis C16,Richardson Eugene T589,Kelly J Daniel146ORCID

Affiliation:

1. Francis I. Proctor Foundation, University of California, San Francisco , San Francisco, California , USA

2. School of Medicine, University of California, San Francisco , San Francisco, California , USA

3. School of Medicine, Stanford University , Stanford, California , USA

4. Institute for Global Health Sciences, University of California , San Francisco, California , USA

5. Partners in Health , Freetown , Sierra Leone

6. Department of Epidemiology and Biostatistics, University of California , San Francisco, California , USA

7. Division of Social Sciences, Doerr School of Sustainability and the Environment, Stanford University , Stanford, California , USA

8. Department of Global Health and Social Medicine, Harvard Medical School , Boston, Massachusetts , USA

9. Department of Medicine, Brigham and Women's Hospital , Boston, Massachusetts , USA

Abstract

Abstract Background Transmission by unreported cases has been proposed as a reason for the 2013–2016 Ebola virus (EBOV) epidemic decline in West Africa, but studies that test this hypothesis are lacking. We examined a transmission chain within social networks in Sukudu village to assess spread and transmission burnout. Methods Network data were collected in 2 phases: (1) serological and contact information from Ebola cases (n = 48, including unreported); and (2) interviews (n = 148), including Ebola survivors (n = 13), to identify key social interactions. Social links to the transmission chain were used to calculate cumulative incidence proportion as the number of EBOV-infected people in the network divided by total network size. Results The sample included 148 participants and 1522 contacts, comprising 10 social networks: 3 had strong links (>50% of cases) to the transmission chain: household sharing (largely kinship), leisure time, and talking about important things (both largely non-kin). Overall cumulative incidence for these networks was 37 of 311 (12%). Unreported cases did not have higher network centrality than reported cases. Conclusions Although this study did not find evidence that explained epidemic decline in Sukudu, it excluded potential reasons (eg, unreported cases, herd immunity) and identified 3 social interactions in EBOV transmission.

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Oncology

Reference41 articles.

1. Rapid intervention to reduce Ebola transmission in a remote village—Gbarpolu County, Liberia, 2014;Blackley;MMWR Morb Mortal Wkly Rep,2015

2. Minimally symptomatic infection in an Ebola ‘hotspot’: a cross-sectional serosurvey;Richardson;PLoS Negl Trop Dis,2016

3. The Ebola outbreak, 2013–2016: old lessons for new epidemics;Coltart;Philos Trans R Soc B Biol Sci,2017

4. Exposure patterns driving Ebola transmission in West Africa: a retrospective observational study;International Ebola Response Team;PLoS Med,2016

5. Anatomy of a hotspot: chain and seroepidemiology of Ebola virus transmission, Sukudu, Sierra Leone, 2015–16;Kelly;J Infect Dis,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3