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. 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 using the posterior distribution obtained from the clinical evaluation data based on the plaque reduction neutralization test. 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 a reasonable interval estimator. As of the $30$th of October $2020$, the $95\%$ credible interval of the estimated SARS-CoV-2 positive population does not exceed $307,448$, approximately $0.6\%$ of the Korean population.
翻译:在2020美元中,韩国疾病控制和预防署报告了三轮关于韩国严重急性呼吸系统综合症冠状病毒2 (SARS-COV-2)抗体血清阳性的调查,我们用一种贝叶斯方法分析了血清阳性调查,事先对血清阳性参数以及诊断测试的敏感性和特殊性进行了信息传播;我们利用基于减少红板中和试验的临床评价数据得出的后部分配数据,在事先构建了信息;已知已确认的2019年科罗纳病毒病例引起的血清阳性参数的制约,可以自然地在拟议的巴伊西亚模式中强制实施;我们还证明,基于Rao试验的血清阳性参数的置信度间隔可以是空的,而巴伊斯方法则提供了合理的间隔估计值;截至10月30日2020美元中,估计的SAS-COV-2阳性人口的95 美元可靠间隔不超过307 448美元,大约0.6美元。