Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification

Biometrics ◽  
2015 ◽  
Vol 72 (1) ◽  
pp. 299-302 ◽  
Author(s):  
Yaakov Malinovsky ◽  
Paul S. Albert ◽  
Anindya Roy
Keyword(s):  
Group Testing ◽  
Testing Algorithms ◽  
Reader Reaction ◽  
Group Testing Algorithms

Rejoinder to “Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification”

Biometrics ◽  
2015 ◽  
Vol 72 (1) ◽  
pp. 303-304 ◽  
Author(s):  
Christopher S. McMahan ◽  
Joshua M. Tebbs ◽  
Christopher R. Bilder
Keyword(s):  
Group Testing ◽  
Testing Algorithms ◽  
Reader Reaction ◽  
Group Testing Algorithms

Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error

Biometrics ◽  
2007 ◽  
Vol 63 (4) ◽  
pp. 1152-1163 ◽  
Author(s):  
Hae-Young Kim ◽  
Michael G. Hudgens ◽  
Jonathan M. Dreyfuss ◽  
Daniel J. Westreich ◽  
Christopher D. Pilcher
Keyword(s):  
Group Testing ◽  
Test Error ◽  
Case Identification ◽  
Testing Algorithms ◽  
Group Testing Algorithms

scholarly journals Cutting testing costs by the pooling design

2020 ◽  
Vol 68 (4) ◽  
pp. 743-759
Author(s):  
Dimitrije Čvokić
Keyword(s):  
Large Scale ◽  
Group Testing ◽  
Current Situation ◽  
Resource Usage ◽  
Pooling Design ◽  
Splitting Algorithm ◽  
The Matrix ◽  
Binary Splitting ◽  
Testing Algorithms ◽  
Group Testing Algorithms

Introduction/purpose: The purpose of group testing algorithms is to provide a more rational resource usage. Therefore, it is expected to improve the efficiency of large-scale COVID-19 screening as well. Methods: Two variants of non-adaptive group testing approaches are presented: Hwang's generalized binary-splitting algorithm and the matrix strategy. Results: The positive and negative sides of both approaches are discussed. Also, the estimations of the maximum number of tests are given. The matrix strategy is presented with a particular modification which reduces the corresponding estimation of the maximum number of tests and which does not affect the complexity of the procedure. This modification can be interesting from the applicability viewpoint. Conclusion: Taking into account the current situation, it makes sense to consider these methods in order to achieve some resource cuts in testing, thus making the epidemiological measures more efficient than they are now.

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