Huang Fengfeng, Guo Pengfei, Wang Yulan
School of Management and Economics, University of Electronic Science and Technology of China, China.
College of Business, City University of Hong Kong, Kowloon, Hong Kong.
Omega. 2022 Oct;112:102689. doi: 10.1016/j.omega.2022.102689. Epub 2022 May 22.
We analyze the group testing strategy that maximizes the efficiency of the SARS-CoV-2 screening test while ensuring its effectiveness, where the effectiveness of group testing guarantees that negative results from pooled samples can be considered presumptive negative. Two aspects of test efficiency are considered, one concerning the maximization of the welfare throughput and the other concerning the maximization of the identification rate (namely, identifying as many infected individuals as possible). We show that compared with individual testing, group testing leads to a higher probability of false negative results but a lower probability of false positive results. To ensure the test effectiveness, both the group size and the prevalence of SARS-CoV-2 must be below certain respective thresholds. To achieve test efficiency that concerns either the welfare throughput maximization or the identification rate maximization, the optimal group size is jointly determined by the test accuracy parameters, the infection prevalence rate, and the relative importance of identifying infected subjects. We also show that the optimal group size that maximizes the welfare throughput is weakly smaller than the one that maximizes the identification rate.
我们分析了一种群组检测策略,该策略在确保新冠病毒筛查检测有效性的同时,最大化其效率,其中群组检测的有效性保证了混合样本的阴性结果可被视为推定阴性。我们考虑了检测效率的两个方面,一个是关于福利通量的最大化,另一个是关于识别率的最大化(即尽可能多地识别出感染个体)。我们表明,与个体检测相比,群组检测导致假阴性结果的概率更高,但假阳性结果的概率更低。为确保检测的有效性,群组规模和新冠病毒的流行率都必须低于各自特定的阈值。为实现关乎福利通量最大化或识别率最大化的检测效率,最优群组规模由检测准确性参数、感染流行率以及识别感染对象的相对重要性共同决定。我们还表明,使福利通量最大化的最优群组规模略小于使识别率最大化的最优群组规模。