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Improvement of Sensitivity of Pooling Strategies for COVID-19
Author(s) -
Hongbin Chen,
Jun-Yi Guo,
YuChen Shu,
Yu-Hsun Lee,
Fei-Huang Chang
Publication year - 2021
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2021/6636396
Subject(s) - pooling , sensitivity (control systems) , grid , covid-19 , computer science , set (abstract data type) , group testing , range (aeronautics) , statistics , data mining , mathematical optimization , algorithm , mathematics , artificial intelligence , medicine , pathology , materials science , geometry , electronic engineering , disease , combinatorics , infectious disease (medical specialty) , engineering , composite material , programming language
Group testing (or pool testing), for example, Dorfman's method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.

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