Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population.
Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population.
复制标题
建模在部分接种人群中SARS-COV-2 DELTA变体的传播。
DOI：
10.3390/v14010158
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发表时间：
2022-01-16
期刊：
影响因子：
--
通讯作者：
Huang K
中科院分区：
文献类型：
--
作者：
Avila-Ponce de León U;Avila-Vales E;Huang K
在正在接种疫苗的人群中，大流行的轨迹取决于病毒如何在未接种疫苗和接种疫苗的个体中传播，这些个体基于不同的天然和疫苗诱导免疫水平表现出不同的传播动态。我们开发了一个数学模型，该模型考虑了亚群和免疫参数，包括疫苗接种率，疫苗有效性和保护的逐渐丧失。该模型预测了在不同的传播和疫苗接种率下SARS-CoV-2 delta变体在美国的传播。我们进一步获得了对照繁殖数，并进行了敏感性分析，以确定每个参数如何影响病毒传播。尽管我们的模型有几个局限性，但与接种疫苗的亚群相比，未接种疫苗的亚群中感染个体的数量要大得多（约10倍）。我们的研究结果表明，加强疫苗诱导的免疫力和预防性行为措施（如戴口罩和接触者追踪）的结合可能需要减缓传染性SARS-CoV-2变种的传播。
In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters, including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Although our model has several limitations, the number of infected individuals was shown to be a magnitude greater (~10×) in the unvaccinated subpopulation compared to the vaccinated subpopulation. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures like face mask-wearing and contact tracing will likely be required to deaccelerate the spread of infectious SARS-CoV-2 variants.
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影响因子：
3.9
作者：
Amaral, Fabio;Casaca, Wallace;Cuminato, Jose A.
通讯作者：
Cuminato, Jose A.
DOI：
10.1016/j.lanepe.2021.100252
发表时间：
2022-01
期刊：
The Lancet regional health. Europe
影响因子：
--
作者：
Allen H;Vusirikala A;Flannagan J;Twohig KA;Zaidi A;Chudasama D;Lamagni T;Groves N;Turner C;Rawlinson C;Lopez-Bernal J;Harris R;Charlett A;Dabrera G;Kall M;COVID-19 Genomics UK (COG-UK Consortium)
通讯作者：
COVID-19 Genomics UK (COG-UK Consortium)
影响因子：
82.9
作者：
Giordano G;Colaneri M;Di Filippo A;Blanchini F;Bolzern P;De Nicolao G;Sacchi P;Colaneri P;Bruno R
通讯作者：
Bruno R
影响因子：
64.5
作者：
Grubaugh ND;Hodcroft EB;Fauver JR;Phelan AL;Cevik M
通讯作者：
Cevik M
影响因子：
29.9
作者：
Haug, Nils;Geyrhofer, Lukas;Klimek, Peter
通讯作者：
Klimek, Peter