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估算意大利 SARS-CoV-2 变异株的代时:基于每日发病率。

Estimating generation time of SARS-CoV-2 variants in Italy from the daily incidence rate.

机构信息

Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy.

The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan.

出版信息

Sci Rep. 2023 Jul 17;13(1):11543. doi: 10.1038/s41598-023-38327-y.

DOI:10.1038/s41598-023-38327-y
PMID:37460598
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10352265/
Abstract

The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy, we find an average generation time [Formula: see text] days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value [Formula: see text] days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter [Formula: see text], was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore show that the novel approach can indicate the existence of virus variants, resulting particularly useful in situations when information about genomic sequencing is not yet available. At the same time, we find that the standard deviation of the generation time does not significantly change among variants.

摘要

病毒传播参数的识别对于确定最佳公共卫生策略至关重要。这些参数可能由于遗传突变或病毒重组而随时间发生显著变化,因此需要持续监测。在这里,我们提出了一种方法,该方法仅使用每日报告病例数作为唯一信息,适用于这项任务。该方法基于感染后时间模型,通过似然函数的高效最大化程序来获取传播参数。将该方法应用于意大利的 SARS-CoV-2 数据,我们发现平均代际时间[Formula: see text]天,在这个时间窗口内,大多数感染可以归因于奥密克戎变体。与此同时,我们发现代际时间[Formula: see text]天的显著较大值,在传播主要由德尔塔变体主导的时间窗口内。我们还能够表明,奥密克戎变体的存在,其特点是较短的[Formula: see text],在 2021 年 12 月的第一周就已经可以检测到,这与国家数据库中报告的 SARS-CoV-2 基因组序列的结果完全一致。因此,我们的结果表明,新方法可以指示病毒变体的存在,在基因组测序信息尚不可用的情况下尤其有用。同时,我们发现变体之间的代际时间标准差没有显著变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/3832d38f2fcb/41598_2023_38327_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/6088b725b4cd/41598_2023_38327_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/d0d87dd69534/41598_2023_38327_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/90ef946d7f12/41598_2023_38327_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/3832d38f2fcb/41598_2023_38327_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/6088b725b4cd/41598_2023_38327_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/d0d87dd69534/41598_2023_38327_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/90ef946d7f12/41598_2023_38327_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a85/10352265/3832d38f2fcb/41598_2023_38327_Fig4_HTML.jpg

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