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Spatial and temporal dynamics of superspreading events in the 2014–2015 West Africa Ebola epidemic

机译:2014-2015年西非埃博拉疫情超扩散事件的时空动态

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摘要

The unprecedented scale of the Ebola outbreak in Western Africa (2014–2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion ( ∼ 61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.
机译:西部非洲埃博拉疫情规模空前(2014-2015年),促使人们为了解该病毒的传播动态并分析可能的遏制策略的性能而进行了大量努力。模型主要集中在疾病的繁殖数量上,该数量代表随机传染性个体产生的继发感染的平均数量。但是,这些人口水平的估计值可能会混淆受感染个体产生的病例数量的重要系统变化,尤其是在空间局部传播和超扩散事件中。尽管超级传播在埃博拉传播的第一手叙述中具有突出的特征,但其动力学尚未得到系统的表征,这阻碍了对未来流行病预测和针对性干预措施的探索。我们使用贝叶斯模型推论将个体水平的空间信息与基于社区的病例(在临床护理系统中未检测到)的其他流行病学数据进行整合,并明确推断每个受感染个体所产生病例的分布。我们的结果表明,超级传播者在维持流行病的继续传播中起着关键作用,它们对很大比例的感染(〜61%)负责。我们的结果还表明,年龄是超级传播的关键人口统计学预测指标。我们还表明,基于社区的病例可能比临床护理系统中通知的病例进展更快,并且大多数传播事件发生在相对较短的距离内(中位值为2.51 km)。我们的结果强调了表征埃博拉病毒超级扩散的重要性,增强了我们对埃博拉病毒时空动态的当前理解,并突出了有针对性的控制措施的潜在重要性。

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