Drivers of African Filovirus (Ebola and Marburg) Outbreaks.
Drivers of African Filovirus (Ebola and Marburg) Outbreaks.
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非洲丝状病毒（埃博拉病毒和马尔堡病毒）爆发的驱动因素。
DOI：
10.1089/vbz.2022.0020
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Drake,JohnM
中科院分区：
文献类型：
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作者：
Stephens,PatrickR;Sundaram,Mekala;Ferreira,Susana;Gottdenker,Nicole;Nipa,KanizFatema;Schatz,AnnakateM;Schmidt,JohnPaul;Drake,JohnM
非洲丝状病毒的爆发往往死亡率很高，2014-2016 年西非埃博拉疫情期间，28,562 例病例中有 11,000 多人死亡。许多研究调查了导致个别丝状病毒爆发的因素，但这项工作很少进行定量综合。此外，丝状病毒爆发的典型原因与其他人畜共患病的典型原因的描述仍然很少。在这项研究中，我们使用 48 个候选因果驱动因素的标题来量化与 45 次非洲丝状病毒（埃博拉病毒和马尔堡病毒）爆发相关的因素。对于丝状病毒爆发，我们审查了超过 700 个同行评审的灰色文献来源，并制定了一份据报道导致每次爆发的因素列表（即每次爆发的“驱动因素概况”）。我们将丝状病毒暴发的概况与 200 起背景暴发进行比较和对比，这些背景暴发是从包含 4463 起细菌和病毒人畜共患疾病暴发的全球数据库中随机选择的。我们还测试了我们观察到的定量模式对于六个协变量、国内生产总值、人口密度和纬度等国家级因素的影响是否稳健，这些因素已被证明会使全球疫情数据产生偏差。我们发现，无论模型中是否包含或排除协变量，丝状病毒爆发的驱动因素都与背景爆发的驱动因素不同。与对照组相比，丝状病毒爆发时更频繁地报告贸易和旅行、野味消费、医疗程序失败以及人类健康基础设施缺陷等社会经济因素。根据我们的结果，我们还对至少 10% 的丝状病毒爆发中报告的驱动程序进行了审查，并提供了每个驱动程序的示例。
Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014–2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a “driver profile” for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.