In 2020, Korea Disease Control and Prevention Agency reported three rounds of surveys on seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in South Korea. SARS-CoV-2 is the virus which inflicts the coronavirus disease 2019 (COVID-19). We analyze the seroprevalence surveys using a Bayesian method with an informative prior distribution on the seroprevalence parameter, and the sensitivity and specificity of the diagnostic test. We construct the informative prior of the sensitivity and specificity of the diagnostic test using the posterior distribution obtained from the clinical evaluation data. The constraint of the seroprevalence parameter induced from the known confirmed coronavirus 2019 cases can be imposed naturally in the proposed Bayesian model. We also prove that the confidence interval of the seroprevalence parameter based on the Rao’s test can be the empty set, while the Bayesian method renders interval estimators with coverage probability close to the nominal level. As of the 30th of October 2020, the \(\,\,\,\,\,\,95\%\,\,\,\,\,\,\) credible interval of the estimated SARS-CoV-2 positive population does not exceed 318, 685, approximately \(0.62\%\) of the Korean population.

2020年，韩国疾病管理和预防机构报告了韩国三轮严重急性呼吸综合征冠状病毒2（SARS-CoV-2）抗体血清阳性率的调查。 SARS-CoV-2 是导致 2019 年冠状病毒病 (COVID-19) 的病毒。我们使用贝叶斯方法分析血清阳性率调查，该方法具有关于血清阳性率参数的信息先验分布以及诊断测试的敏感性和特异性。我们使用从临床评估数据获得的后验分布构建诊断测试的敏感性和特异性的信息先验。从已知确诊的 2019 冠状病毒病例中得出的血清流行率参数的约束可以自然地施加在所提出的贝叶斯模型中。我们还证明基于 Rao 检验的血清流行率参数的置信区间可以是空集，而贝叶斯方法呈现的区间估计量的覆盖概率接近名义水平。截至 2020 年 10 月 30 日，估计 SARS-CoV-2 的\(\,\,\,\,\,\,95\%\,\,\,\,\,\,\)可信区间阳性人口不超过318人、685人，约占韩国人口的0.62% 。