The major indicator of the efficiency of a pool-testing strategy is the reduction of the number of assays for a certain number of specimens. The analysis process takes a long time, consumes reagents and requires significant manual labor. Other related processes require much less time and cost, and in most cases, a tenfold reduction in the number of assays increases the throughput of the laboratory by 9 times and also decreases the cost of the testing of one person by 9 times.
The number of assays primarily depends on prevalence (the proportion of positives samples) and the maximum pool size (the limit of dilution of a specimen). The lower the prevalence, the larger the pool size that can be used, and ultimately, fewer assays will be required. For example, for 1% prevalence, the maximal effective pool size is 69, for 10% prevalence it is 7 and for prevalence above 25% the pool size becomes less than 4 and the efficiency becomes too low to be worth using.
Different laboratories can operate with different values of prevalence, constraints of testing and priorities of optimization, and these values can change over time. LOMT supports several different pooling strategies and automatically searches for the optimal strategy for every specific condition.
For a high prevalence (10%) and the maximum pool size 8, the maximum reduction of the number of assays is 2.1x compared to the individual testing: for a batch of 1000 specimens the pool testing uses 477 assays on average, while the individual testing uses 1000 assays. For a medium prevalence (1%) and the maximum pool size 32, the maximum reduction of the number of assays is 11x, with 90 assays on avarage for a batch of 1000 specimens. For a low prevalence (0.1%) and the maximum pool size 32, the maximum reduction of the number of assays is 25x, with 39 assays on average for a batch of 1000 specimens.