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Classification of SARS-CoV-2 Variants in Turkey

Year 2022, Volume: 6 Issue: 1, 1092 - 1101, 30.06.2022

Hilal Arslan

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes the COVID-19 disease, which turns into a pandemic and threatens public health. Appearing of SARS-CoV-2 variants show a significant challenge to determine the risk of infection, develop vaccines as well as antiviral agents, monitor the changes, and assess the evolution of SARS-CoV-2. In this study, we propose a method identifying SARS-CoV-2 variants in Turkey. To achieve this goal, nucleotide occurrences are computed from the whole genome sequences that include four nucleotides, A, C, T, and G. Thus, 30 000 bps genome sequences are represented by only four integer numbers. After features are extracted, four classification methods, support vector machines, k-nearest neighbor, neural network, and decision tree are employed to identify SARS-CoV-2 variants. Experimental results are conducted on a dataset including 1403 genome sequences from Turkey and belonging to variants of SARS-CoV-2, B.1.1.7 (Alpha), B.1.351 (Beta), P.1. (Gamma), as well as B.1.617 (Delta). Experimental results present that the KNN classifier achieves an accuracy of 0.94, a precision of 0.81, a recall of 0.80, and an F-score of 0.80 on average.

Keywords

SARS-CoV-2 variants COVID-19 Coronavirus Classifiers COVID-19 variants in Turkey

References

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  • [19] Kandeel, M., Mohamed, M. E. M., Abd El‐Lateef, H. M., Venugopala, K. N., & El‐Beltagi, H. S. (2021). Omicron variant genome evolution and phylogenetics. In Journal of Medical Virology (Vol. 94, Issue 4, pp. 1627–1632). Wiley. https://doi.org/10.1002/jmv.27515
  • [20] Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. In Chaos, Solitons & Fractals (Vol. 139, p. 110017). Elsevier BV. https://doi.org/10.1016/j.chaos.2020.110017
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  • [25] Tao, K., Tzou, P. L., Nouhin, J., Gupta, R. K., de Oliveira, T., Kosakovsky Pond, S. L., Fera, D., & Shafer, R. W. (2021). The biological and clinical significance of emerging SARS-CoV-2 variants. In Nature Reviews Genetics (Vol. 22, Issue 12, pp. 757–773). Springer Science and Business Media LLC. https://doi.org/10.1038/s41576-021-00408-x
  • [26] Faria, N. R. et al. (2021). Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. In Science (Vol. 372, Issue 6544, pp. 815–821). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.abh2644
  • [27] Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data – from vision to reality. In Eurosurveillance (Vol. 22, Issue 13). European Centre for Disease Control and Prevention (ECDC). https://doi.org/10.2807/1560-7917.es.2017.22.13.30494
  • [28] H. Arslan and B. Aygün, "Performance Analysis of Machine Learning Algorithms in Detection of COVID-19 from Common Symptoms," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477809.
  • [29] Harvey, W.T., Carabelli, A.M., Jackson, B. et al. (2021). SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 19, 409–424 (2021). https://doi.org/10.1038/s41579-021-00573-0
  • [30] Arslan, H. (2021). Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data. In The 7th International Management Information Systems Conference. International Management Information Systems Conference. MDPI. https://doi.org/10.3390/proceedings2021074020
  • [31] Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. In Arabian Journal for Science and Engineering (Vol. 46, Issue 9, pp. 8261–8272). Springer Science and Business Media LLC. https://doi.org/10.1007/s13369-020-05212-z
  • [32] Hasan, N. (2020). A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model. In Internet of Things (Vol. 11, p. 100228). Elsevier BV. https://doi.org/10.1016/j.iot.2020.100228
  • [33] Yoo, S. H. et al. (2020). Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. In Frontiers in Medicine (Vol. 7). Frontiers Media SA. https://doi.org/10.3389/fmed.2020.00427

Year 2022, Volume: 6 Issue: 1, 1092 - 1101, 30.06.2022

Hilal Arslan

Abstract

References

  • [1] Volz, E. et al., 2021. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature 593, 266–269.
  • [2] Tegally, H. et al., 2021. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature 592, 438–443.
  • [3] Sabino, E. C. et al., 2021. Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. Lancet 397, 452–455.
  • [4] Mlcochova, P., et al., 2021. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature.
  • [5] Sahoo, J. P., & Samal, K. C., 2021. World on Alert: WHO Designated South African New COVID Strain (Omicron/B. 1.1. 529) as a Variant of Concern. Biotica Research Today, 3(11), 1086-1088.
  • [7] Ali S., Sahoo B., Ullah N., Zelikovskiy A., Patterson M., Khan I. 2021 A k-mer Based Approach for SARS-CoV-2 Variant Identification. In: Wei Y., Li M., Skums P., Cai Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science, vol 13064. Springer, Cham.
  • [8] Noble, W. S., 2006. What is a support vector machine?. Nature biotechnology, 24(12), 1565-1567.
  • [9] Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K., 2003. KNN model-based approach in classification. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 986-996). Springer.
  • [10] Hagan, M. T., Demuth, H. B., & Beale, M., 1997. Neural network design. PWS Publishing Co.
  • [11] Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F., 2011. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
  • [12] Shu, Y., & McCauley, J., 2017. GISAID: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance, 22(13), 30494.
  • [13] Refaeilzadeh, P., Tang, L., & Liu, H., 2009. Cross-validation. Encyclopedia of database systems, 5, 532-538.
  • [14] Khateeb, J., Li, Y., & Zhang, H., 2021. Emerging SARS-CoV-2 variants of concern and potential intervention approaches. In Critical Care (Vol. 25, Issue 1). Springer Science and Business Media LLC.
  • [15] Lauring, A. S., & Malani, P. N., 2021. Variants of SARS-CoV-2. In JAMA (Vol. 326, Issue 9, p. 880). American Medical Association (AMA).
  • [16] Simon-Loriere, E., Schwartz, O. 2022. Towards SARS-CoV-2 serotypes?. Nat Rev Microbiol 20, 187–188.
  • [17] Han, X., & Ye, Q. 2021. The variants of SARS‐CoV‐2 and the challenges of vaccines. In Journal of Medical Virology (Vol. 94, Issue 4, pp. 1366–1372). Wiley.
  • [18] Burioni, R., Topol, E.J. Assessing the human immune response to SARS-CoV-2 variants. Nat Med 27, 571–572, 2021.
  • [19] Kandeel, M., Mohamed, M. E. M., Abd El‐Lateef, H. M., Venugopala, K. N., & El‐Beltagi, H. S. (2021). Omicron variant genome evolution and phylogenetics. In Journal of Medical Virology (Vol. 94, Issue 4, pp. 1627–1632). Wiley. https://doi.org/10.1002/jmv.27515
  • [20] Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. In Chaos, Solitons & Fractals (Vol. 139, p. 110017). Elsevier BV. https://doi.org/10.1016/j.chaos.2020.110017
  • [21] Arslan, H., & Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. In Engineering Science and Technology, an International Journal (Vol. 24, Issue 4, pp. 839–847). Elsevier BV. https://doi.org/10.1016/j.jestch.2020.12.026
  • [22] Arslan, H. (2021). COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. In Computers & Industrial Engineering (Vol. 161, p. 107666). Elsevier BV. https://doi.org/10.1016/j.cie.2021.107666
  • [23] Garcia-Beltran et al.(2021). COVID-19-neutralizing antibodies predict disease severity and survival. In Cell (Vol. 184, Issue 2, pp. 476-488.e11). Elsevier BV. https://doi.org/10.1016/j.cell.2020.12.015
  • [24] Hatirnaz Ng, O., Akyoney, S., Sahin, I., Soykam, H. O., Bayram Akcapinar, G., Ozdemir, O., Kancagi, D. D., Sir Karakus, G., Yurtsever, B., Kocagoz, A. S., Ovali, E., & Ozbek, U. (2021). Mutational landscape of SARS-CoV-2 genome in Turkey and impact of mutations on spike protein structure. In M. Adnan (Ed.), PLOS ONE (Vol. 16, Issue 12, p. e0260438). Public Library of Science (PLoS). https://doi.org/10.1371/journal.pone.0260438
  • [25] Tao, K., Tzou, P. L., Nouhin, J., Gupta, R. K., de Oliveira, T., Kosakovsky Pond, S. L., Fera, D., & Shafer, R. W. (2021). The biological and clinical significance of emerging SARS-CoV-2 variants. In Nature Reviews Genetics (Vol. 22, Issue 12, pp. 757–773). Springer Science and Business Media LLC. https://doi.org/10.1038/s41576-021-00408-x
  • [26] Faria, N. R. et al. (2021). Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. In Science (Vol. 372, Issue 6544, pp. 815–821). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.abh2644
  • [27] Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data – from vision to reality. In Eurosurveillance (Vol. 22, Issue 13). European Centre for Disease Control and Prevention (ECDC). https://doi.org/10.2807/1560-7917.es.2017.22.13.30494
  • [28] H. Arslan and B. Aygün, "Performance Analysis of Machine Learning Algorithms in Detection of COVID-19 from Common Symptoms," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477809.
  • [29] Harvey, W.T., Carabelli, A.M., Jackson, B. et al. (2021). SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 19, 409–424 (2021). https://doi.org/10.1038/s41579-021-00573-0
  • [30] Arslan, H. (2021). Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data. In The 7th International Management Information Systems Conference. International Management Information Systems Conference. MDPI. https://doi.org/10.3390/proceedings2021074020
  • [31] Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. In Arabian Journal for Science and Engineering (Vol. 46, Issue 9, pp. 8261–8272). Springer Science and Business Media LLC. https://doi.org/10.1007/s13369-020-05212-z
  • [32] Hasan, N. (2020). A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model. In Internet of Things (Vol. 11, p. 100228). Elsevier BV. https://doi.org/10.1016/j.iot.2020.100228
  • [33] Yoo, S. H. et al. (2020). Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. In Frontiers in Medicine (Vol. 7). Frontiers Media SA. https://doi.org/10.3389/fmed.2020.00427
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Hilal Arslan 0000-0002-6449-6952

Publication Date June 30, 2022
Submission Date April 8, 2022
Acceptance Date April 25, 2022
Published in Issue Year 2022 Volume: 6 Issue: 1

Cite

APA Arslan, H. (2022). Classification of SARS-CoV-2 Variants in Turkey. Journal of Turkish Operations Management, 6(1), 1092-1101.
AMA Arslan H. Classification of SARS-CoV-2 Variants in Turkey. JTOM. June 2022;6(1):1092-1101.
Chicago Arslan, Hilal. “Classification of SARS-CoV-2 Variants in Turkey”. Journal of Turkish Operations Management 6, no. 1 (June 2022): 1092-1101.
EndNote Arslan H (June 1, 2022) Classification of SARS-CoV-2 Variants in Turkey. Journal of Turkish Operations Management 6 1 1092–1101.
IEEE H. Arslan, “Classification of SARS-CoV-2 Variants in Turkey”, JTOM, vol. 6, no. 1, pp. 1092–1101, 2022.
ISNAD Arslan, Hilal. “Classification of SARS-CoV-2 Variants in Turkey”. Journal of Turkish Operations Management 6/1 (June 2022), 1092-1101.
JAMA Arslan H. Classification of SARS-CoV-2 Variants in Turkey. JTOM. 2022;6:1092–1101.
MLA Arslan, Hilal. “Classification of SARS-CoV-2 Variants in Turkey”. Journal of Turkish Operations Management, vol. 6, no. 1, 2022, pp. 1092-01.
Vancouver Arslan H. Classification of SARS-CoV-2 Variants in Turkey. JTOM. 2022;6(1):1092-101.

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