Table 2.

Research on optimization of pooling approach in COVID-19 screening strategy.

Strategy	Evaluation index	Study
Dorfman pool test Pool test for each sample tested in multiple pools	Daily detection capacity Ntest-savinga Sensitivity Number of sample results represented by each test	Cleary et al [80], 2021
Individual test Pool test repeated multiple times	The number of persons per test The upper bound for the fraction of Nmissb FNRc	Hanel and Thurner [71], 2020
Individual test Dorfman pool test Splitting pool test: samples in the negative pools are recombined to new pool tests, and samples with 2 negative results are identified as negative	NF-posd Nteste NF-negf	Litvak et al [72], 2020
Dorfman pool test Sequential pool test: the positive pool is divided into several subpools of pool size of 3, and the samples in the positive subpool are tested individually	Ntest Ratio of number of tests	Cheng et al [66], 2021
Random sequential pooling test the positive pool is divided into several subpools, and the samples in the positive subpool are tested individually Informed sequential pool test: divide subjects with similar risk of infection (eg, by age and sex into the same pool)	Ntest per person	Millioni and Mortarino [55], 2020
Individual test Binary pool test of the best number and depth of branches considering the prevalence	Ratio of number of tests	Perivolaropoulos and Vlacha [67], 2021
Nested pool test: the positive pool is then divided into several small pools	Ntest per stage Ntest per person Accuracy Cost Ncasesg	Armendáriz et al [68], 2021
Pool test strategy based on the optimization algorithm: the positive pool is then divided into several small pools and tested in the next stage	The percentage of tests required compared with individual testing	Rai et al [69], 2020
Pooling test of pooling size of 20 in the family with different prevalence and then retest with minipool for batches with positive results using pooling size of 20, 10, 5, 4, and 2	Ntest	Ng et al [70], 2022
Multistage pool test: ≥3 stages using the overlap strategy (some samples are detected in both pools)	IRh Ntest	Gu et al [73], 2021
Individual test Dorfman pool test Matrix pool test	Costs Cost per test Positive rate	Kim et al [74], 2022
Individual test Matrix pool test OptReplica pool test: each patient is allocated in the first pool and replicated in another pool with the smallest number of allocated patients	Ntest-saving	Žilinskas et al [75], 2021
Individual test Dorfman pool test Pentagram minipool test: for the positive Dorfman pooling with size of 10, double samples are tested by 5 “three-in-one” pools and 1 “five-in-one” pool	Ntest	Zhou and Zhou [76], 2022
Subsample pool test in the hypercube algorithm	Loss of sensitivity compared with individual test Ntest per person and Ninfectedi	Mutesa et al [77], 2021
Pooling test under different prevalence, edge, and dimension using the hypercubic method	Ntest	Wu et al [78], 2022
Dorfman pool test Recursive pool test Matrix pool test D-Optimal Pool Experimental design (a novel Bayesian pooling strategy)	FNR and FPRj Ntest	Daon et al [79], 2021
Individual test 2-stage Dorfman pool test Binary splitting pool test Optimized recursive binary splitting pool test Matrix pool test Sobel-R1: a decision tree approach based on binomial distribution	Confirmed cases per test Time to test the whole population NT-pos, NF-pos Ncases Number of quarantined individuals	de Wolff et al [81], 2020

^aN_test-saving: number of saving tests compared with individual testing.

^bN_miss: number of missed infections.

^cFNR: false-negative rate.

^dN_F-pos: number of false-positive results.

^eN_test: number of tests.

^fN_F-neg: number of false-negative results.

^gN_cases: number of confirmed cases.

^hIR: infection rate.

ⁱN_infected: number of infected people.

^jFPR: false-positive rate.

^kN_T-pos: number of false-negative results.