Optimal Configuration of a Square Array Group Testing Algorithm
We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upp...
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| Published in | Communications in statistics. Theory and methods Vol. 40; no. 3; pp. 436 - 448 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia, PA
Taylor & Francis Group
01.01.2011
Taylor & Francis Taylor & Francis Ltd |
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| Online Access | Get full text |
| ISSN | 0361-0926 1532-415X |
| DOI | 10.1080/03610920903391303 |
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| Abstract | We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. |
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| AbstractList | We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi.We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 22, 33, and 44 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi. |
| Author | Hudgens, Michael G. Kim, Hae-Young |
| AuthorAffiliation | 1 Department of Biostatistics, University of North Carolina at Chapel Hill 2 Center for Statistical Analysis and Research, New England Research Institutes |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics, University of North Carolina at Chapel Hill – name: 2 Center for Statistical Analysis and Research, New England Research Institutes |
| Author_xml | – sequence: 1 givenname: Michael G. surname: Hudgens fullname: Hudgens, Michael G. email: mhudgens@bios.unc.edu organization: Department of Biostatistics , University of North Carolina at Chapel Hill – sequence: 2 givenname: Hae-Young surname: Kim fullname: Kim, Hae-Young organization: Center for Statistical Analysis and Research, New England Research Institutes |
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| Title | Optimal Configuration of a Square Array Group Testing Algorithm |
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