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利用中国境外的疫情规模估算新冠病毒传播中的过度离散情况。

Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China.

作者信息

Endo Akira, Abbott Sam, Kucharski Adam J, Funk Sebastian

机构信息

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.

The Alan Turing Institute, London, NW1 2DB, UK.

出版信息

Wellcome Open Res. 2020 Jul 10;5:67. doi: 10.12688/wellcomeopenres.15842.3. eCollection 2020.

Abstract

A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number , variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of (2-3), the overdispersion parameter of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for and (95% CrIs: 1.4-12; 0.04-0.2); however, the upper bound of was not well informed by the model and data, which did not notably differ from that of the prior distribution. Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

摘要

新型冠状病毒病(COVID-19)疫情现已蔓延至全球多个国家。虽然人际传播的持续传播链表明基本再生数较高,但二次传播数量的变化(通常以所谓的超级传播事件为特征)可能很大,因为一些国家观察到的本地传播比其他国家少。我们通过将数学模型应用于受影响国家观察到的疫情规模,对COVID-19传播中的个体水平变化进行了量化。我们从世界卫生组织情况报告中提取了受影响国家的输入病例数和本地病例数,并应用了一个分支过程模型,其中假设二次传播的数量遵循负二项分布。我们的模型表明,COVID-19传播中存在高度的个体水平变化。在当前共识范围(2 - 3)内,负二项分布的过度分散参数估计约为0.1(中位数估计值为0.1;对于R0 = 2.5,95%可信区间:0.05 - 0.2),这表明80%的二次传播可能是由一小部分感染个体(约10%)引起的。联合估计得出了R0和过度分散参数的可能范围(95%可信区间:R0为1.4 - 12;过度分散参数为0.04 - 0.2);然而,R0的上限并未得到模型和数据的充分支持,与先验分布没有显著差异。我们发现子代分布高度过度分散,这突出了将干预措施重点放在超级传播上的潜在益处。由于大多数感染个体对疫情传播没有贡献,通过预防相对罕见的超级传播事件,有效再生数可能会大幅降低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3842/7355215/705325d28bc2/wellcomeopenres-5-17714-g0000.jpg

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