Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2022, 5(9); doi: 10.25236/AJCIS.2022.050904.

Optimized Solution for Nucleic Acid Detection Based on Group Testing Model


Bo Shu1, Ruochong Xiong1, Hongjin Xiao2

Corresponding Author:
Bo Shu

1College of Science, Minzu University of China, Beijing, 100081 China

2College of Information Engineering, Minzu University of China, Beijing, 100081, China


Under the critical situation of recurrent new crown outbreaks, large-scale full-scale nucleic acid testing has become the norm across the country. When the total number of people in a region is large and the infection rate is small, mixed testing can greatly improve testing efficiency and save costs compared with one-person testing. For the optimization of the mixed testing model, the Group Testing strategy is used to analyze the mixed testing method with the minimum number of tests, build a model and calculate the results with the help of Matlab, and finally obtain the optimal ratio of mixed testing in each region.


Nucleic acid mixing test; Group Testing; Overlap test; Optimization model

Cite This Paper

Bo Shu, Ruochong Xiong, Hongjin Xiao. Optimized Solution for Nucleic Acid Detection Based on Group Testing Model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 9: 22-27. https://doi.org/10.25236/AJCIS.2022.050904.


[1] Cicalese F. Group Testing [J]. Monographs in Theoretical Computer Science An Eatcs, 2013.

[2] Li, Chou Hsiung (June 1962). "A sequential method for screening experimental variables".Journal of the American Statistical Association. 57(298): 455–477.

[3] Ding-Zhu, Du; Hwang, Frank K. (1993). Combinatorial group testing and its applications. Singapore: World Scientific. ISBN 978-9810212933. 

[4] Xie H, Cheng HZ, Niu DX. An algorithm for discretization of continuous attributes of rough sets based on information entropy [J]. Journal of Computer Science, 2005, 28(9): 5.

[5] Xiao Shengxie, Lv Enlin. Mathematical expectations of discrete interval probability random variables and fuzzy probability random variables [J]. Applied Mathematics and Mechanics, 2005, 26(10):8.