论文标题
贝叶斯对受感染患者的贝叶斯推断和患病率估计
Bayesian inference of infected patients in group testing with prevalence estimation
论文作者
论文摘要
小组测试是一种通过对从患者收集的标本池进行测试来识别受感染患者的方法。对于测试以有限概率返回错误结果的情况，我们提出了贝叶斯推断和相应的信念传播（BP）算法，以从池上执行的测试结果中识别受感染的患者。我们表明，考虑到每个患者的点估计值的可靠间隔，可以提高真正的阳性率。此外，通过将预期最大化方法与BP算法相结合来估算测试中的患病率和误差概率。作为另一种方法，我们引入了分层贝叶斯模型，以识别受感染的患者并估计患病率。通过比较这些方法，我们为实际使用指南制定了指南。
Group testing is a method of identifying infected patients by performing tests on a pool of specimens collected from patients. For the case in which the test returns a false result with finite probability, we propose Bayesian inference and a corresponding belief propagation (BP) algorithm to identify the infected patients from the results of tests performed on the pool. We show that the true-positive rate is improved by taking into account the credible interval of a point estimate of each patient. Further, the prevalence and the error probability in the test are estimated by combining an expectation-maximization method with the BP algorithm. As another approach, we introduce a hierarchical Bayes model to identify the infected patients and estimate the prevalence. By comparing these methods, we formulate a guide for practical usage.
