Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.
Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.
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DOI：
10.1371/journal.pmed.1002170
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发表时间：
2016-11
期刊：
影响因子：
15.8
通讯作者：
Yoti Z
中科院分区：
文献类型：
--
作者：
International Ebola Response Team;Agua-Agum J;Ariyarajah A;Aylward B;Bawo L;Bilivogui P;Blake IM;Brennan RJ;Cawthorne A;Cleary E;Clement P;Conteh R;Cori A;Dafae F;Dahl B;Dangou JM;Diallo B;Donnelly CA;Dorigatti I;Dye C;Eckmanns T;Fallah M;Ferguson NM;Fiebig L;Fraser C;Garske T;Gonzalez L;Hamblion E;Hamid N;Hersey S;Hinsley W;Jambei A;Jombart T;Kargbo D;Keita S;Kinzer M;George FK;Godefroy B;Gutierrez G;Kannangarage N;Mills HL;Moller T;Meijers S;Mohamed Y;Morgan O;Nedjati-Gilani G;Newton E;Nouvellet P;Nyenswah T;Perea W;Perkins D;Riley S;Rodier G;Rondy M;Sagrado M;Savulescu C;Schafer IJ;Schumacher D;Seyler T;Shah A;Van Kerkhove MD;Wesseh CS;Yoti Z
西非埃博拉疫情于2013年12月在几内亚开始，可能是由单一的人畜共患病传入。由于最初的控制努力无效，出现了规模空前的埃博拉疫情。截至2015年5月4日，它已导致超过19，000例可能和确诊的埃博拉病例，主要发生在几内亚（3，529），利比里亚（5，343）和塞拉利昂（10，746）。在这里，我们对疫情期间收集的数据进行了分析，确定了传播的驱动因素，并强调了可以改进控制的领域。截至2015年5月4日，西非报告了超过19 000例确诊和可能的埃博拉病例。确诊或可能感染埃博拉病毒的个人（“病例”）被问及他们在患病前是否在葬礼或非葬礼场合接触过其他潜在的埃博拉病例（“潜在来源接触者”）。我们对病例行列表进行了回顾性分析，这些病例行列表是从已报告给世卫组织的病例调查表的国家数据库中整理出来的。这些分析最初是为了协助世卫组织在疫情期间的应对工作而进行的，现已更新以供发表。我们分析了几内亚3,529例、利比里亚5,343例和塞拉利昂10,746例病例的数据; 33%的病例报告了暴露。报告葬礼暴露的病例比例随着时间的推移而下降。我们发现，在给定地区的给定月份，这一比例与地区内传播强度（由估计的繁殖数量（R）量化）之间呈正相关（r = 0.35，p < 0.001）。我们还发现R与症状发作≤4天内住院病例的地区比例之间存在负相关（r =-0.37，p < 0.001）。这两个比例没有相关性，这表明减少葬礼出席和更快的住院独立影响当地的传播强度。我们能够将14%的潜在源接触者确定为病例行列表中的病例。将病例与可能感染他们的接触者联系起来提供了关于传播网络的信息。这揭示了推断传播的高度异质性，只有20%的病例占至少73%的新感染，这种现象通常被称为超级传播。多变量回归模型使我们能够确定被命名为潜在源接触的预测因素。葬礼和非葬礼接触者的情况相似：症状严重、死亡、未住院、年龄较大和症状发作前旅行。非葬礼暴露在接触者死亡前后达到峰值。有证据表明，住院治疗减少，但没有消除继续接触。我们发现，埃博拉治疗单位在防止住院和死亡人员接触方面优于其他医疗机构。我们分析的主要局限性是数据质量有限，病例未输入数据库，病例未报告暴露，或数据输入错误（特别是日期和可能的错误分类）。实现消灭埃博拉病毒的目标是具有挑战性的，部分原因是超级传播。安全的葬礼做法和快速住院治疗有助于遏制埃博拉疫情。持续的实时数据采集、报告和分析对于跟踪传播模式、通知资源部署，从而加速和维持从人群中消除病毒至关重要。在这项回顾性观察研究中，Christophe Fraser及其同事分析了2013-2016年西非埃博拉疫情期间与埃博拉传播相关的暴露模式。了解个人如何以及从谁那里获得感染可以帮助通知应对措施，以限制流行病的影响;这项研究提供了最初为协助2013-2016年西非埃博拉疫情期间的国际应对而进行的分析的更新版本。截至2015年5月4日，西非报告了超过19，000例确诊或可能的埃博拉病例（“病例”）。这些病例被问及在患病前是否在葬礼或非葬礼场合接触过潜在的埃博拉病例（“潜在的源头接触者”）。我们分析了几内亚3,529例、利比里亚5,343例和塞拉利昂10,746例病例的数据; 33%的病例报告了暴露。非葬礼暴露在接触者死亡前后达到高峰。有证据表明超级传播，只有20%的病例占新感染病例的至少73%。安全的葬礼做法和快速住院治疗有助于遏制埃博拉疫情。尽管这三个国家的情况具有挑战性，但数据非常详细;然而，分析受到数据质量的限制，大多数数据缺失和输入错误。鉴于病毒在宿主中的持久性，必须保持对埃博拉疫情的积极监测和分析，以避免和遏制未来的疫情。
The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved. Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (“cases”) were asked if they had exposure to other potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO’s response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = −0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population. In this retrospective observational study, Christophe Fraser and colleagues analyze the exposure patterns associated with Ebola transmission during the 2013-2016 epidemic in West Africa. Knowing how and from whom individuals acquire infection can help inform the response to limit the impact of an epidemic; this study presents updated versions of analyses initially performed to assist the international response during the 2013–2016 Ebola epidemic in West Africa. Over 19,000 individuals with confirmed or probable Ebola (“cases”) were reported in West Africa by 4 May 2015. Cases were asked whether they had exposure to potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. Non-funeral exposures were strongly peaked around the time of death of the contact. There was evidence of super-spreading, with only 20% of cases accounting for at least 73% of new infections. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. The data are highly detailed despite the challenging circumstances in the three countries; however, the analyses were limited by data quality, mostly missing data and incorrect entries. In light of viral persistence in reservoirs, it is vital to maintain active surveillance and analysis of Ebola outbreaks to avoid and contain future outbreaks.
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影响因子：
11.8
作者：
Lindblade KA;Kateh F;Nagbe TK;Neatherlin JC;Pillai SK;Attfield KR;Dweh E;Barradas DT;Williams SG;Blackley DJ;Kirking HL;Patel MR;Dea M;Massoudi MS;Wannemuehler K;Barskey AE;Zarecki SL;Fomba M;Grube S;Belcher L;Broyles LN;Maxwell TN;Hagan JE;Yeoman K;Westercamp M;Forrester J;Mott J;Mahoney F;Slutsker L;DeCock KM;Nyenswah T
通讯作者：
Nyenswah T
DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
DOI：
10.1126/science.1259657
发表时间：
2014-09-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gire SK;Goba A;Andersen KG;Sealfon RS;Park DJ;Kanneh L;Jalloh S;Momoh M;Fullah M;Dudas G;Wohl S;Moses LM;Yozwiak NL;Winnicki S;Matranga CB;Malboeuf CM;Qu J;Gladden AD;Schaffner SF;Yang X;Jiang PP;Nekoui M;Colubri A;Coomber MR;Fonnie M;Moigboi A;Gbakie M;Kamara FK;Tucker V;Konuwa E;Saffa S;Sellu J;Jalloh AA;Kovoma A;Koninga J;Mustapha I;Kargbo K;Foday M;Yillah M;Kanneh F;Robert W;Massally JL;Chapman SB;Bochicchio J;Murphy C;Nusbaum C;Young S;Birren BW;Grant DS;Scheiffelin JS;Lander ES;Happi C;Gevao SM;Gnirke A;Rambaut A;Garry RF;Khan SH;Sabeti PC
通讯作者：
Sabeti PC
影响因子：
64.8
作者：
Lloyd-Smith JO;Schreiber SJ;Kopp PE;Getz WM
通讯作者：
Getz WM
DOI：
10.1093/infdis/jiv304
发表时间：
2015-12-01
期刊：
The Journal of infectious diseases
影响因子：
--
作者：
Fitzpatrick G;Vogt F;Moi Gbabai OB;Decroo T;Keane M;De Clerck H;Grolla A;Brechard R;Stinson K;Van Herp M
通讯作者：
Van Herp M