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从完整疫情暴发中无模型估计 COVID-19 传播动态。

Model-free estimation of COVID-19 transmission dynamics from a complete outbreak.

机构信息

School of Maths and Stats, University of Canterbury, Christchurch, New Zealand.

Te Pūnaha Matatini, Auckland, New Zealand.

出版信息

PLoS One. 2021 Mar 24;16(3):e0238800. doi: 10.1371/journal.pone.0238800. eCollection 2021.

DOI:10.1371/journal.pone.0238800
PMID:33760817
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7990215/
Abstract

New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower probability of transmitting the disease in comparison to adults and the elderly. Imported cases infected fewer people on average and also had a lower probability of transmitting than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65-85% of transmission. Subclinical cases infected fewer individuals than clinical cases. After controlling for outliers serial intervals were approximated with a normal distribution (μ = 4.4 days, σ = 4.7 days). Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19.

摘要

新西兰在消除病毒传播之前有 1499 例 COVID-19 病例。在疫情爆发期间,广泛的接触者追踪导致了一个具有流行病学关联病例的数据集。这些数据包含了有关病毒传播动力学的有用信息,包括其对年龄等因素的依赖性,以及对不同控制措施的反应。我们使用蒙特卡罗网络构建技术来估计每个感染者在疫情期间的二次感染人数。然后,我们应用标准统计技术来量化个体之间的差异。10 岁以下的儿童在病例数据中明显代表性不足。与成年人和老年人相比,儿童感染的人数更少,传播疾病的可能性也更低。输入病例的平均感染人数和传播概率也低于本地获得的病例。超级传播者是疫情动态的一个重要因素,成年人中的 20%的病例占 65-85%的传播。无症状病例比有症状病例感染的人数更少。在控制了异常值后,序列间隔近似正态分布(μ=4.4 天,σ=4.7 天)。边境管控和强有力的社会隔离措施,特别是针对超级传播者的措施,在减少 COVID-19 的传播方面发挥了重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/1b4f795e229c/pone.0238800.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/12d476b8c07e/pone.0238800.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/cbaebcb837bf/pone.0238800.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/93a2da6b93d7/pone.0238800.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/1b4f795e229c/pone.0238800.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/12d476b8c07e/pone.0238800.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/cbaebcb837bf/pone.0238800.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/93a2da6b93d7/pone.0238800.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/770d/7990215/1b4f795e229c/pone.0238800.g007.jpg

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