Tropical Group Testing

被引:5
作者
Wang, Hsin-Po [1 ,2 ]
Gabrys, Ryan [1 ,3 ]
Vardy, Alexander [1 ]
机构
[1] Univ Calif San Diego, Dept Elect & Comp Engn ECE, San Diego, CA 92093 USA
[2] Univ Calif Berkeley, Dept Elect Engn & Comp Sci EECS, Berkeley, CA 94720 USA
[3] Univ Calif San Diego, Naval Informat Warfare Ctr San Diego, San Diego, CA 92093 USA
关键词
Testing; DNA; Electron tubes; Delays; Polymers; Liquids; Heating systems; Group testing; polymerase chain reaction (PCR) testing; tropical arithmetic; VIRAL LOAD; DEFECTIVE MEMBERS; LOWER BOUNDS; SARS-COV-2; CONSTRUCTION; MATRICES;
D O I
10.1109/TIT.2023.3282847
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Polymerase chain reaction (PCR) testing is the gold standard for diagnosing COVID-19. PCR amplifies the virus DNA 40 times to produce measurements of viral loads that span seven orders of magnitude. Unfortunately, the outputs of these tests are imprecise and therefore quantitative group testing methods, which rely on precise measurements, are not applicable. Motivated by the ever-increasing demand to identify individuals infected with SARS-CoV-19, we propose a new model that leverages tropical arithmetic to characterize the PCR testing process. Our proposed framework, termed tropical group testing, overcomes existing limitations of quantitative group testing by allowing for imprecise test measurements. In many cases, some of which are highlighted in this work, tropical group testing is provably more powerful than traditional binary group testing in that it requires fewer tests than classical approaches, while additionally providing a mechanism to identify the viral load of each infected individual. It is also empirically stronger than related works that have attempted to combine PCR, quantitative group testing, and compressed sensing.
引用
收藏
页码:6098 / 6120
页数:23
相关论文
共 89 条
[1]  
Abdalhamid B, 2020, AM J CLIN PATHOL, V153, P715, DOI [10.1101/2020.04.03.20050195, 10.1093/ajcp/aqaa064, 10.1093/AJCP/AQAA064]
[2]  
Acharya J, 2017, IEEE INT SYMP INFO, P2353, DOI 10.1109/ISIT.2017.8006950
[3]  
Aigner M, 2018, Proofs from THE BOOK., V6th
[4]  
Aldridge M., 2022, Pooled testing and its applications in the COVID-19 pandemic, P217, DOI DOI 10.1007/978-3-030-78334-1_11
[5]   Group Testing: An Information Theory Perspective [J].
Aldridge, Matthew ;
Johnson, Oliver ;
Scarlett, Jonathan .
FOUNDATIONS AND TRENDS IN COMMUNICATIONS AND INFORMATION THEORY, 2019, 15 (3-4) :196-392
[6]   Group Testing Algorithms: Bounds and Simulations [J].
Aldridge, Matthew ;
Baldassini, Leonardo ;
Johnson, Oliver .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2014, 60 (06) :3671-3687
[7]  
[Anonymous], 1999, Combinatorial Group Testing and Its Applications, DOI DOI 10.1142/4252
[8]  
[Anonymous], 1993, Series on Applied Mathematics
[9]   The Limit of Detection Matters: The Case for Benchmarking Severe Acute Respiratory Syndrome Coronavirus 2 Testing [J].
Arnaout, Ramy ;
Lee, Rose A. ;
Lee, Ghee Rye ;
Callahan, Cody ;
Cheng, Annie ;
Yen, Christina F. ;
Smith, Kenneth P. ;
Arora, Rohit ;
Kirby, James E. .
CLINICAL INFECTIOUS DISEASES, 2021, 73 (09) :E3042-E3046
[10]  
Austin D., 2020, Pooling Strategies for COVID-19 Testing