SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency.
SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency.
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DOI：
10.1093/aje/kwad106
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发表时间：
2023-09-01
影响因子：
5
通讯作者：
中科院分区：
文献类型：
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作者：
血清调查是衡量严重急性呼吸综合征冠状病毒2（SARS-CoV-2）人群暴露的关键资源。越来越多的证据表明，无症状和轻度感染（占所有感染的95%以上）与严重感染相比，抗体滴度较低。抗体水平也在感染后几周达到峰值，并逐渐衰减。我们开发了一种统计方法，从原始血清阳性率调查结果中估计累积发病率，以解释这些谱偏倚的来源。我们纳入了从感染后的纵向队列，沿着与流行病的时间序列，以占血清调查的时间相对于最近的个人可能已经感染的多个检测抗体反应的数据。我们应用这种方法对不同环境和研究设计的5项大规模SARS-CoV-2血清调查的累积发病率进行了估计。我们在一些调查的结果中发现了原始血清阳性率和超过2倍的累积发病率之间的实质性差异，并且我们为从业者提供了一种工具，可以使用预设或自定义参数值生成累积发病率估计值。虽然在过去的一年里，人们做出了前所未有的努力来估计SARS-CoV-2的血清阳性率，但对这些研究结果的解释需要适当考虑人群水平的流行病学背景和个人水平的免疫动力学。
Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.
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DOI：
10.1016/s2214-109x(21)00026-7
发表时间：
2021-05
期刊：
The Lancet. Global health
影响因子：
--
作者：
Chen X;Chen Z;Azman AS;Deng X;Sun R;Zhao Z;Zheng N;Chen X;Lu W;Zhuang T;Yang J;Viboud C;Ajelli M;Leung DT;Yu H
通讯作者：
Yu H
DOI：
10.1111/rssc.12435
发表时间：
2020-08-13
影响因子：
1.6
作者：
Gelman, Andrew;Carpenter, Bob
通讯作者：
Carpenter, Bob
影响因子：
2.6
作者：
Gardner, IA;Stryhn, H;Collins, MT
通讯作者：
Collins, MT
在很大程度上不受限制的流行病中，巴西亚马逊中SARS-COV-2的四分之三攻击率。
DOI：
10.1126/science.abe9728
发表时间：
2021-01-15
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Buss LF;Prete CA Jr;Abrahim CMM;Mendrone A Jr;Salomon T;de Almeida-Neto C;França RFO;Belotti MC;Carvalho MPSS;Costa AG;Crispim MAE;Ferreira SC;Fraiji NA;Gurzenda S;Whittaker C;Kamaura LT;Takecian PL;da Silva Peixoto P;Oikawa MK;Nishiya AS;Rocha V;Salles NA;de Souza Santos AA;da Silva MA;Custer B;Parag KV;Barral-Netto M;Kraemer MUG;Pereira RHM;Pybus OG;Busch MP;Castro MC;Dye C;Nascimento VH;Faria NR;Sabino EC
通讯作者：
Sabino EC
影响因子：
4.3
作者：
Gostic, Katelyn M;McGough, Lauren;Cobey, Sarah
通讯作者：
Cobey, Sarah