The brief comparison of the operational efficiency of pool-testing strategies for COVID-19 mass testing in PCR laboratories

This paper addresses the operational efficiency of different pool-testing strategies in typical scenarios of a PCR laboratory working in mass testing for COVID-19 with different values of prevalence, limitations and conditions of testing, and priorities of optimization. The research employs a model of the laboratorys testing process, created after interviewing of PCR laboratories and studying their operations. The limitations and operational characteristics of this model were applied in a simulation of the testing process with different pool-testing strategies managed by a computer program developed in the LOMT project. The efficiency indicators assessed are the number of assays needed to obtain results of a batch of specimens, the number of specimens identified after the first analysis, and total time to obtain all results. Depending on prevalence, constraints of testing, and priorities of optimization, different pool-testing strategies provide the best operational efficiency. The binary splitting algorithm provides the maximum reduction of the number of assays: from 1.99x reduction for a high prevalence (10%) to 25x reduction for a low prevalence (0.1%), while other algorithms provide the least amount of time to obtain results or the maximum number of the specimens classified after the first analysis.