Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys.
Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys.
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DOI：
10.7554/elife.64206
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发表时间：
2021-03-05
期刊：
影响因子：
7.7
通讯作者：
Grad YH
中科院分区：
文献类型：
--
作者：
Larremore DB;Fosdick BK;Bubar KM;Zhang S;Kissler SM;Metcalf CJE;Buckee CO;Grad YH
确定有多少人感染了SARS-CoV-2仍然是控制COVID-19大流行的当务之急。血清学检测可以识别过去的感染，可用于估计累积发病率，但各种采样策略的相对准确性和鲁棒性尚不清楚。我们开发了一个灵活的框架，整合了测试特征，样本量和异质性的不确定性，在不同亚群的血清阳性率比较估计抽样方案。使用相同的框架，并假设血清阳性表明免疫保护，我们通过动态模型传播估计和不确定性，以评估评估公共卫生干预措施所需的流行病学参数的不确定性，并发现人口统计和接触网络的抽样方案优于均匀抽样。该框架可以适应于优化血清学调查设计给定的测试特性和能力，人口统计学，抽样策略和建模方法，并可以定制，以支持决策周围引入或删除干预措施。
Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies have been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.