Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.
Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.
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通过遗传距离分析快速评估 COVID-19 疫苗针对 SARS-CoV-2 变体症状感染的有效性
DOI：
10.1038/s41591-022-01877-1
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发表时间：
2022-08
期刊：
影响因子：
82.9
通讯作者：
Wang, Maggie Haitian
中科院分区：
文献类型：
--
作者：
Cao, Lirong;Lou, Jingzhi;Chan, See Yeung;Zheng, Hong;Liu, Caiqi;Zhao, Shi;Li, Qi;Mok, Chris Ka Pun;Chan, Renee Wan Yi;Chong, Marc Ka Chun;Wu, William Ka Kei;Chen, Zigui;Wong, Eliza Lai Yi;Chan, Paul Kay Sheung;Zee, Benny Chung Ying;Yeoh, Eng Kiong;Wang, Maggie Haitian
及时评估2019冠状病毒病（COVID-19）疫苗对严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）变异株的保护作用，是制定流行病控制规划的迫切需要。基于来自49项研究的78个疫苗效力或有效性（VE）数据和从31个地区收集的1，984，241个SARS-CoV-2序列，我们分析了流行病毒对疫苗株的遗传距离（GD）与对症状感染的VE之间的关系。我们发现，SARS-CoV-2刺突蛋白受体结合结构域的GD对疫苗保护具有高度预测性，在基于疫苗平台的混合效应模型中占VE变化的86.3%（P = 0.038），在基于疫苗平台的模型中占87.9%（P = 0.006）。我们应用VE-GD模型预测现有疫苗介导的针对新遗传变异的保护作用，并通过已发表的真实世界和临床试验数据验证了结果，发现预测的VE与观察到的VE高度一致。我们使用mRNA疫苗平台估计针对Delta变体的VE为82.8%（95%预测区间：68.7-96.0），与观察性研究报告的VE 83.0%非常匹配。在Omicron的四个亚系中，使用mRNA疫苗平台，预测的VE在11.9%和33.3%之间变化，其中对BA.1预测的VE最高，对BA.2预测的VE最低。VE-GD框架能够真实的预测疫苗保护，并提供针对新变体的快速评估方法，可为疫苗部署和公共卫生反应提供信息。在获得真实世界的有效性数据之前，预测COVID-19疫苗对传播的SARS-CoV-2变体的有效性的模型可能有助于指导公共卫生和研究对新的令人担忧的变体做出更快的反应。
Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7–96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses. A model that predicts the effectiveness of COVID-19 vaccines against circulating SARS-CoV-2 variants, before the acquisition of real-world effectiveness data, may help guide more rapid public health and research responses to new variants of concern.
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DOI：
10.1056/nejmoa2113017
发表时间：
2021-11-04
期刊：
The New England journal of medicine
影响因子：
--
作者：
El Sahly HM;Baden LR;Essink B;Doblecki-Lewis S;Martin JM;Anderson EJ;Campbell TB;Clark J;Jackson LA;Fichtenbaum CJ;Zervos M;Rankin B;Eder F;Feldman G;Kennelly C;Han-Conrad L;Levin M;Neuzil KM;Corey L;Gilbert P;Janes H;Follmann D;Marovich M;Polakowski L;Mascola JR;Ledgerwood JE;Graham BS;August A;Clouting H;Deng W;Han S;Leav B;Manzo D;Pajon R;Schödel F;Tomassini JE;Zhou H;Miller J;COVE Study Group
通讯作者：
COVE Study Group
DOI：
10.1056/nejmoa2119451
发表时间：
2022-04-21
期刊：
The New England journal of medicine
影响因子：
--
作者：
Andrews N;Stowe J;Kirsebom F;Toffa S;Rickeard T;Gallagher E;Gower C;Kall M;Groves N;O'Connell AM;Simons D;Blomquist PB;Zaidi A;Nash S;Iwani Binti Abdul Aziz N;Thelwall S;Dabrera G;Myers R;Amirthalingam G;Gharbia S;Barrett JC;Elson R;Ladhani SN;Ferguson N;Zambon M;Campbell CNJ;Brown K;Hopkins S;Chand M;Ramsay M;Lopez Bernal J
通讯作者：
Lopez Bernal J
DOI：
10.1056/nejmoa2105290
发表时间：
2021-12-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
Falsey AR;Sobieszczyk ME;Hirsch I;Sproule S;Robb ML;Corey L;Neuzil KM;Hahn W;Hunt J;Mulligan MJ;McEvoy C;DeJesus E;Hassman M;Little SJ;Pahud BA;Durbin A;Pickrell P;Daar ES;Bush L;Solis J;Carr QO;Oyedele T;Buchbinder S;Cowden J;Vargas SL;Guerreros Benavides A;Call R;Keefer MC;Kirkpatrick BD;Pullman J;Tong T;Brewinski Isaacs M;Benkeser D;Janes HE;Nason MC;Green JA;Kelly EJ;Maaske J;Mueller N;Shoemaker K;Takas T;Marshall RP;Pangalos MN;Villafana T;Gonzalez-Lopez A;AstraZeneca AZD1222 Clinical Study Group
通讯作者：
AstraZeneca AZD1222 Clinical Study Group
DOI：
10.1056/nejmoa2109072
发表时间：
2021-10-14
期刊：
The New England journal of medicine
影响因子：
--
作者：
Bergwerk M;Gonen T;Lustig Y;Amit S;Lipsitch M;Cohen C;Mandelboim M;Levin EG;Rubin C;Indenbaum V;Tal I;Zavitan M;Zuckerman N;Bar-Chaim A;Kreiss Y;Regev-Yochay G
通讯作者：
Regev-Yochay G
影响因子：
64.5
作者：
Cui Z;Liu P;Wang N;Wang L;Fan K;Zhu Q;Wang K;Chen R;Feng R;Jia Z;Yang M;Xu G;Zhu B;Fu W;Chu T;Feng L;Wang Y;Pei X;Yang P;Xie XS;Cao L;Cao Y;Wang X
通讯作者：
Wang X