Abstract
The COVID-19 pandemic has adversely affected millions all over the world. Efficient and effective testing of individuals for COVID-19, via modalities such as reverse transcription polymerase chain reaction (RT-PCR) is a crucial factor in combating this menace. Given the widespread scarcity of testing resources including testing kits, reagents, skilled manpower and available time, pooled testing has been advocated as a method of speed-up. Pooling involves mixing together small portions of ‘samples’ of different individuals, followed by testing the pools instead of the individual samples. It has been observed that a much smaller number of pools, as compared to the number of samples, is sufficient to allow for accurate prediction of the health status of the constituent samples, under the common and reasonable assumption that only a small number of the samples were infected. Artificial intelligence (AI) has emerged as a key tool in improving the prediction accuracy as well as efficiency of pooled testing. Such algorithmic tools are often studied within the frameworks of group testing and compressed sensing. In this chapter, we present algorithmic tools for pooled testing and recovery, giving a broad description of the use of AI for pooled testing in the context of COVID-19.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Salathé M, Althaus CL, Neher R, Stringhini S, Hodcroft E, Fellay J, et al. COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation. Swiss Med Wkly. 2020;150(11–12):w20225.
Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049–55.
Lucia C, Federico PB, Alejandra GC. An ultrasensitive, rapid, and portable coronavirus SARS-CoV-2 sequence detection method based on CRISPR-Cas12. bioRxiv. 2020;2020:1127.
Ben-Assa N, Naddaf R, Gefen T, Capucha T, Hajjo H, Mandelbaum N, et al. SARS-CoV-2 on-the-spot virus detection directly from patients. medRxiv. 2020;2020:2389.
Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1(3):1559–82.
Yelin I, Aharony N, Shaer-Tamar E, Argoetti A, Messer E, Berenbaum D, et al. Evaluation of COVID-19 RT-qPCR test in multi-sample pools. MedRxiv. 2020;2020:2389.
Hanel R, Thurner S. Boosting test-efficiency by pooled testing strategies for SARS-CoV-2. 2020. https://arxiv.org/abs/2003.09944.
Gilbert AC, Iwen MA, Strauss MJ. Group testing and sparse signal recovery. In: 2008 42nd Asilomar conference on signals, systems and computers. Piscataway, NJ: IEEE; 2008. p. 1059–63.
Eldar YC, Kutyniok G. Compressed sensing: theory and applications. Cambridge: Cambridge University Press; 2012.
Eldar YC. Sampling theory: beyond bandlimited systems. Cambridge: Cambridge University Press; 2015.
Shental N, Levy S, Skorniakov S, Wuvshet V, Shemer-Avni Y, Porgador A, et al. Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers. Sci Adv. 2020;6(37):eabc5961.
Ghosh S, Agarwal R, Rehan MA, Pathak S, Agarwal P, Gupta Y, et al. A compressed sensing approach to pooled RT-PCR testing for COVID-19 detection. IEEE Open J Signal Process. 2021;2:248–64.
Yi J, Mudumbai R, Xu W. Low-cost and high-throughput testing of COVID-19 viruses and antibodies via compressed sensing: system concepts and computational experiments. 2020. https://arxiv.org/abs/2004.05759.
Petersen HB, Bah B, Jung P. Practical high-throughput, non-adaptive and noise-robust SARS-CoV-2 testing. 2020. https://arxiv.org/abs/2007.09171.
Yi J, Cho M, Wu X, Xu W, Mudumbai R. Error correction codes for COVID-19 virus and antibody testing: using pooled testing to increase test reliability. 2020. https://arxiv.org/abs/2007.14919.
Zhu J, Rivera K, Baron D. Noisy pooled PCR for virus testing. 2020. https://arxiv.org/abs/2004.02689.
Jawerth N. How is the COVID-19 virus detected using real time RT-PCR? https://www.iaea.org/newscenter/news/how-is-the-covid-19-virus-detected-using-real-time-rt-pcr.
Efficiency of Real-Time PCR. https://www.thermofisher.com/in/en/home/life-science/pcr/real-time-pcr/real-time-pcr-learning-center/real-time-pcr-basics/efficiency-real-time-pcr-qpcr.html. Accessed 5 Apr 2021.
Buchan B, et al. Distribution of SARS-CoV-2 PCR cycle threshold values provide practical insight into overall and target-specific sensitivity among symptomatic patients. Am J Clin Pathol. 2020;154(4):479–85.
Gevertz J, Dunn S, Roth C. Mathematical model of real-time PCR kinetics. Biotechnol Bioeng. 2005;92(3):346–55.
Wyllie AL. Saliva or nasopharyngeal swab specimens for detection of SARS-CoV-2. N Engl J Med. 2020;383(13):1283–6.
Allen JWL, Verkerke H, Owens J, Saeedi B, Boyer D, Shin S, et al. Serum pooling for rapid expansion of anti-SARS-CoV-2 antibody testing capacity. Transfus Clin Biol. 2021;28(1):51–4.
Hughes-Oliver JM, Swallow WH. A two-stage adaptive group-testing procedure for estimating small proportions. J Am Stat Assoc. 1994;89(427):982–93.
Dorfman R. The detection of defective members of large populations. Ann Math Stat. 1943;14(4):436–40.
Du D, Hwang FK, Hwang F. Combinatorial group testing and its applications, vol. 12. Singapore: World Scientific; 2000.
Macula AJ. Probabilistic nonadaptive group testing in the presence of errors and DNA library screening. Ann Comb. 1999;3(1):61–9.
Varanasi MK. Group detection for synchronous Gaussian code-division multiple-access channels. IEEE Trans Inf Theory. 1995;41(4):1083–96.
Cheraghchi M, Karbasi A, Mohajer S, Saligrama V. Graph-constrained group testing. IEEE Trans Inf Theory. 2012;58(1):248–62.
Wu S, Wei S, Wang Y, Vaidyanathan R, Yuan J. Partition information and its transmission over boolean multi-access channels. IEEE Trans Inf Theory. 2014;61(2):1010–27.
Bajwa WU, Haupt JD, Sayeed AM, Nowak RD. Joint source–channel communication for distributed estimation in sensor networks. IEEE Trans Inf Theory. 2007;53(10):3629–53.
Clifford R, Efremenko K, Porat E, Rothschild A. k-mismatch with don’t cares. In: European symposium on algorithms. Berlin: Springer; 2007. p. 151–62.
Tsai WT, Chen Y, Cao Z, Bai X, Huang H, Paul R. Testing web services using progressive group testing. In: Advanced workshop on content computing. Berlin: Springer; 2004. p. 314–22.
Cormode G, Muthukrishnan S. What’s new: finding significant differences in network data streams. IEEE/ACM Trans Netw. 2005;13(6):1219–32.
Xuan Y, Shin I, Thai MT, Znati T. Detecting application denial-of-service attacks: a group-testing-based approach. IEEE Trans Parallel Distrib Syst. 2009;21(8):1203–16.
Goodrich MT, Atallah MJ, Tamassia R. Indexing information for data forensics. In: International conference on applied cryptography and network security. Berlin: Springer; 2005. p. 206–21.
Chan CL, Jaggi S, Saligrama V, Agnihotri S. Non-adaptive group testing: explicit bounds and novel algorithms. IEEE Trans Inf Theory. 2014;60(5):3019–35.
Cohen A, Shlezinger N, Solomon A, Eldar YC, Médard M. Multi-level group testing with application to one-shot pooled COVID-19 tests. In: Proc. IEEE ICASSP; 2021.
Ben-Knaan EF, Shlezinger N, Eldar YC. Recovery of noisy pooled tests via learned factor graphs with application to COVID-19 testing. In: Proc. IEEE ICASSP; 2022.
Ben-Ami R, Klochendler A, Seidel M, Sido T, Gurel-Gurevich O, Yassour M, et al. Large-scale implementation of pooled RNA extraction and RT-PCR for SARS-CoV-2 detection. Clin Microbiol Infect. 2020;26(9):1248–53.
Aldridge M, Johnson O, Scarlett J. Group testing: an information theory perspective. Found Trends Commun Inf Theory. 2019;15(3–4):196–392.
Sobel M, Groll PA. Group testing to eliminate efficiently all defectives in a binomial sample. Bell Syst Tech J. 1959;38(5):1179–252.
Hu M, Hwang F, Wang JK. A boundary problem for group testing. SIAM J Algebraic Discrete Methods. 1981;2(2):81–7.
Atia GK, Saligrama V. Boolean compressed sensing and noisy group testing. IEEE Trans Inf Theory. 2012;58(3):1880–901.
Kautz W, Singleton R. Nonrandom binary superimposed codes. IEEE Trans Inf Theory. 1964;10(4):363–77.
Chan CL, Che PH, Jaggi S, Saligrama V. Non-adaptive probabilistic group testing with noisy measurements: near-optimal bounds with efficient algorithms. In: 2011 49th Annual Allerton conference on communication, control, and computing (Allerton). Piscataway, NJ: IEEE; 2011. p. 1832–9.
Aldridge M, Baldassini L, Johnson O. Group testing algorithms: bounds and simulations. IEEE Trans Inf Theory. 2014;60(6):3671–87.
Weiss Y, Freeman WT. On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs. IEEE Trans Inf Theory. 2001;47(2):736–44.
Kschischang FR, Frey BJ, Loeliger HA. Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory. 2001;47(2):498–519.
MacKay DJ. Information theory, inference and learning algorithms. Cambridge: Cambridge University Press; 2003.
Shlezinger N, Farsad N, Eldar YC, Goldsmith AJ. Inference from stationary time sequences via learned factor graphs. IEEE Trans Signal Process. 2022.
Davenport M, Duarte M, Eldar Y, Kutyniok G. Introduction to compressed sensing. In: Eldar Y, Kutyniok G, editors. Compressed sensing: theory and applications. Cambridge: Cambridge University Press; 2012. p. 1–64.
Candes E. The restricted isometry property and its implications for compressive sensing. Comptes Rendus Math. 2008;346(9–10):589–92.
Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the LASSO and generalizations. Boca Raton: CRC Press; 2015.
Baraniuk R, Davenport M, DeVore R, Wakin M. A simple proof of the restricted isometry property for random matrices. Constr Approx. 2008;28:253–63.
Benatia D, Godefroy R, Lewis J. Estimating COVID-19 prevalence in the United States: a sample selection model approach. medRxiv. 2020;2020:2942. https://doi.org/10.1101/2020.04.20.20072942v1.
Shental N, et al. Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers. Sci Adv. 2020;6(37):5961.
Ghosh S, et al. Tapestry: a single-round smart pooling technique for COVID-19 testing. medRxiv. 2020;2020:727. https://doi.org/10.1101/2020.04.23.20077727v1.
Wipf D, Rao BD. Sparse Bayesian learning for basis selection. IEEE Trans Signal Process. 2004;52(8):2153–64.
Kueng R, Jung P. Robust nonnegative sparse recovery and the nullspace property of 0/1 measurements. IEEE Trans Inf Theory. 2018;64(2):689–703.
Pati Y, Rezaiifar R, Krishnaprasad P. Orthogonal matching pursuit: recursive function approximation with application to wavelet decomposition. In: Asilomar Conference on signals, systems and computing; 1993. p. 40–4.
Yaghoobi M, Wu D, Davies M. Fast non-negative orthogonal matching pursuit. IEEE Signal Process Lett. 2015;22(9):1229–33.
Heidarzadeh A, Narayanan K. Two-stage adaptive pooling with RT-qPCR for COVID-19 screening. In: ICASSP; 2021.
Nida H, Blum S, Zielinski D, Srivastava DA, Elbaum R, Xin Z, et al. Highly efficient de novo mutant identification in a Sorghum bicolor TILLING population using the ComSeq approach. Plant J. 2016;86:349–59.
Shental N, Amir A, Zuk O. Identification of rare alleles and their carriers using compressed se(que)nsing. Nucleic Acids Res. 2010;38(79):e179.
Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci. 2(1):183–202.
Tipping M. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res. 2001;1:211–44.
Crespo Marques E, Maciel N, Naviner L, Cai H, Yang J. A review of sparse recovery algorithms. IEEE Access. 2019;7:1300–22.
Goenka R, Cao SJ, Wong CW, Rajwade A, Baron D. Contact tracing information improves the performance of group testing algorithms. 2021. https://arxiv.org/abs/2106.02699.
Reed IS, Solomon G. Polynomial codes over certain finite fields. J Soc Ind Appl Math. 1960;8(2):300–4.
Naidu RR, Jampana P, Sastry CS. Deterministic compressed sensing matrices: construction via euler squares and applications. IEEE Trans Signal Process. 2016;64(14):3566–75.
Naidu RR, Murthy CR. Construction of binary sensing matrices using extremal set theory. IEEE Signal Process Lett. 2017;24(2):211–5.
DeVore RA. Deterministic constructions of compressed sensing matrices. J Complex. 2007;23(4):918–25.
Candes E, Wakin M. An introduction to compressive sampling. IEEE Signal Process Mag. 2008;25(2):21–30.
Bioglio V, Bianchi T, Magli E. On the fly estimation of the sparsity degree in compressed sensing using sparse sensing matrices. In: ICASSP. 2015:3801–5.
Ravazzi C, Fosson S, Bianchi T, Magli E. Sparsity estimation from compressive projections via sparse random matrices. EURASIP J Adv Signal Process. 2018;56:578.
Center for Disease Control and Prevention. Contact tracing for COVID-19. https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/contact-tracing.html.
Case Investigation and Contact Tracing. Part of a multipronged approach to fight the COVID-19 pandemic. 2021. https://www.cdc.gov/coronavirus/2019-ncov/php/principles-contact-tracing.html.
Hekmati A, Ramachandran G, Krishnamachari B. CONTAIN: privacy-oriented contact tracing protocols for epidemics. https://arxiv.org/abs/2004.05251.
Kleinman R, Merkel C. Digital contact tracing for COVID-19. Can Med Assoc J. 2020;192(24):E653–6.
Hohman M, McMaster F, Woodruff SI. Contact tracing for COVID-19: the use of motivational interviewing and the role of social work. Clin Soc Work J. 2021;49(4):419–28.
Ross AM, Zerden L, Ruth B, Zelnick J, Cederbaum J. Contact tracing: an opportunity for social work to lead. Soc Work Public Health. 2020;35(7):533–45.
Goenka R, Cao SJ, Wong CW, Rajwade A, Baron D. Contact tracing enhances the efficiency of COVID-19 Group Testing. In: ICASSP; 2021; pp. 8168–8172.
Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B. 2007;68(1):49–67.
Jacob L, Obozinski G, Vert JP. Group LASSO with overlap and graph LASSO. In: Proceedings of the 26th Annual International Conference on machine learning. 2009.
Nikolopoulos P, Srinivasavaradhan SR, Guo T, Fragouli C, Diggavi S. Group testing for connected communities. In: AISTATS; 2021.
Nikolopoulos P, Srinivasavaradhan SR, Guo T, Fragouli C, Diggavi S. Group testing for overlapping communities. In: IEEE International conference on communications 2021; pp. 1–7.
Attia M, Chang W, Tandon R. Heterogeneity aware two-stage group testing. IEEE Trans Signal Process. 2021;69:3977–90.
Bilder CR, Tebbs JM, Chen P. Informative retesting. J Am Stat Assoc. 2010;105(491):942–55.
McMahan CS, Tebbs JM, Bilder CR. Informative Dorfman screening. Biometrics. 2012;68(1):287–96.
Deckert A, Barnighausen T, Kyei NN. Simulation of pooled-sample analysis strategies for COVID-19 mass testing. Bull World Health Organ. 2020;98(9):590.
Arasli B, Ulukus S. Group testing for overlapping communities. 2021. https://arxiv.org/abs/2101.05792.
Ahn S, Chen WN, Ozgur A. Adaptive group testing on networks with community structure. 2021. https://arxiv.org/abs/2101.02405.
Lendle SD, Hudgens MG, Qaqish BF. Group testing for case identification with correlated responses. Biometrics. 2012;68:532–40.
Lin YJ, Yu CH, Liu TH, Chang CS, Chen WT. Positively correlated samples save pooled testing costs. 2020. https://arxiv.org/abs/2011.09794.
List of countries implementing pool testing strategy against COVID-19. https://en.wikipedia.org/wiki/List_of_countries_implementing_pool_testing_strategy_against_COVID-19.
IIT-Bombay professor comes up with new tool for Covid testing. It cuts time and cost. 2021. https://theprint.in/health/iit-bombay-professor-comes-up-with-new-tool-for-covid-testing-it-cuts-time-and-cost/684709/.
Deka S, Kalita D. Effectiveness of sample pooling strategies for SARS-CoV-2 mass screening by RT-PCR: a scoping review. J Lab Physicians. 2020;12(03):212–8.
Gatta VL, Moscato V, Postiglione M, Sperli G. An epidemiological neural network exploiting dynamic graph structured data applied to the COVID-19 outbreak. IEEE Trans Big Data. 2021;7(1):45–55.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rajwade, A., Shlezinger, N., Eldar, Y.C. (2022). AI for Pooled Testing of COVID-19 Samples. In: Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-031-08506-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-08506-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08505-5
Online ISBN: 978-3-031-08506-2
eBook Packages: MedicineMedicine (R0)