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本文引用的文献

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Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.莫桑比克霍乱风险的快速预测:转化的挑战与机遇。
Prehosp Disaster Med. 2019 Oct;34(5):557-562. doi: 10.1017/S1049023X19004783. Epub 2019 Sep 3.
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Real-time Epidemic Forecasting: Challenges and Opportunities.实时疫情预测:挑战与机遇。
Health Secur. 2019 Jul/Aug;17(4):268-275. doi: 10.1089/hs.2019.0022.
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On the predictability of infectious disease outbreaks.传染病爆发的可预测性。
Nat Commun. 2019 Feb 22;10(1):898. doi: 10.1038/s41467-019-08616-0.
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Differential mobility and local variation in infection attack rate.感染发病率的差异迁移和局部变化。
PLoS Comput Biol. 2019 Jan 22;15(1):e1006600. doi: 10.1371/journal.pcbi.1006600. eCollection 2019 Jan.
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Reconstruction and prediction of viral disease epidemics.病毒性疾病流行的重建与预测。
Epidemiol Infect. 2018 Nov 5;147:e34. doi: 10.1017/S0950268818002881.
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Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.人口流动性降低与塞拉利昂埃博拉疫情期间的旅行限制有关:利用移动电话数据。
Int J Epidemiol. 2018 Oct 1;47(5):1562-1570. doi: 10.1093/ije/dyy095.
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The potential impact of case-area targeted interventions in response to cholera outbreaks: A modeling study.针对霍乱疫情的病例地区靶向干预的潜在影响:一项建模研究。
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Evidence-based guidelines for supportive care of patients with Ebola virus disease.埃博拉病毒病患者支持性护理的循证指南。
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The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.RAPIDD 埃博拉预测挑战赛:综合分析及经验教训
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潜伏期影响塞拉利昂霍乱和埃博拉疫情的空间可预测性。

Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone.

机构信息

Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115.

Institute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears - Consell Superior d'Investigacions Científiques, E-07122 Palma de Mallorca, Spain.

出版信息

Proc Natl Acad Sci U S A. 2020 Mar 3;117(9):5067-5073. doi: 10.1073/pnas.1913052117. Epub 2020 Feb 13.

DOI:10.1073/pnas.1913052117
PMID:32054785
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7060667/
Abstract

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen's underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen's incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

摘要

在疫情爆发期间预测传染病的时空传播是疫情应对的一个重要组成部分。然而,这在方法上和数据需求方面都具有挑战性,因为疾病传播受到许多因素的影响,包括病原体的潜在传播参数和流行病学动态、社会网络和人口连通性以及环境条件。在这里,我们使用来自塞拉利昂的数据,分析了最近霍乱和埃博拉疫情的时空动态,并比较和对比了这两种病原体在同一人群中的传播。我们开发了一种传染病空间传播的模拟模型,以研究病原体潜伏期对传播动态和疫情可预测性的影响。我们发现,仅潜伏期的差异就可以决定具有不同自然史的疾病的可预测性极限,无论是从经验上还是从我们的模拟中都是如此。我们的研究结果表明,潜伏期较长的疾病,如埃博拉病毒,感染者在变得具有传染性之前可以传播得更远,因此与潜伏期较短的疾病(如霍乱)更可预测的波浪式传播相比,会导致更多的远距离触发事件和更不可预测的疾病轨迹。