通过血清学不确定性和日发病率估算SARS-CoV-2抗体流行率。

Pub Date : 2022-09-01 Epub Date: 2022-08-04 DOI:10.1002/cjs.11722
Liangliang Wang, Joosung Min, Renny Doig, Lloyd T Elliott, Caroline Colijn
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引用次数: 0

摘要

SARS-CoV-2血清学检测为估计过去感染过的个体数量(包括常规检测未发现的病例,这在大流行期间和不同司法管辖区有所不同)提供了一个范例。在我们只有有限的血清学数据并且没有考虑到血清学测试的不确定性的情况下,这样的估计是具有挑战性的。在这项工作中,我们提供了一个联合贝叶斯模型,通过整合多个数据源,血清学检测的敏感性和特异性先验,以及一个有效的流行病学动力学模型,来改进对血清患病率(SARS-CoV-2抗体人群比例)的估计。我们将模型应用于加拿大不列颠哥伦比亚省的大温哥华地区,使用了2020年1月底至5月大流行期间获得的数据。我们估计的血清患病率与以前的文献一致,但可信区间更窄。
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Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence.

Serology tests for SARS-CoV-2 provide a paradigm for estimating the number of individuals who have had an infection in the past (including cases that are not detected by routine testing, which has varied over the course of the pandemic and between jurisdictions). Such estimation is challenging in cases for which we only have limited serological data and do not take into account the uncertainty of the serology test. In this work, we provide a joint Bayesian model to improve the estimation of the sero-prevalence (the proportion of the population with SARS-CoV-2 antibodies) through integrating multiple sources of data, priors on the sensitivity and specificity of the serological test, and an effective epidemiological dynamics model. We apply our model to the Greater Vancouver area, British Columbia, Canada, with data acquired during the pandemic from the end of January to May 2020. Our estimated sero-prevalence is consistent with previous literature but with a tighter credible interval.

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