A Review on the Use of Machine Learning Against the Covid-19 Pandemic

Authors

Volume: 12 | Issue: 1 | Pages: 8039-8044 | February 2022 | https://doi.org/10.48084/etasr.4628

Abstract

Coronavirus-2019 disease (Covid-19) is a contagious respiratory disease that emerged in late 2019 and has been recognized by the World Health Organization (WHO) as a global pandemic in early 2020. Since then, researchers have been exploring various strategies and techniques to fight against this outbreak. The point when the pandemic appeared was also a period in which Machine Learning (ML) and Deep Learning (DL) algorithms were competing with traditional technologies, leading to significant findings in diverse domains. Consequently, many researchers employed ML/DL to speed up Covid-19 detection, prevention, and treatment. This paper reviews the state-of-the-art ML/DL tools used, thoroughly evaluating these techniques and their impact on the battle against Covid-19. This article aims to provide valuable insight to the researchers to assess the use of ML against the Covid-19 pandemic.

Keywords:

Covid-19, machine learning, deep learning, evaluation, coronavirus, detection, prevention, treatment

Downloads

Download data is not yet available.

References

J. P. Cohen, P. Morrison, and L. Dao, "COVID-19 Image Data Collection," arXiv:2003.11597 [cs, eess, q-bio], Mar. 2020, Accessed: Dec. 13, 2021. [Online]. Available: http://arxiv.org/abs/2003.11597.

X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, "COVID-CT-Dataset: A CT Scan Dataset about COVID-19," arXiv:2003.13865 [cs, eess, stat], Jun. 2020, Accessed: Dec. 13, 2021. [Online]. Available: http://arxiv.org/abs/2003.13865.

M. Jun et al., "COVID-19 CT Lung and Infection Segmentation Dataset." Zenodo, Apr. 20, 2020.

The National Library of Medicine, "MedPix." https://medpix.nlm.nih.gov/home (accessed Dec. 13, 2021).

"LUNA16 - Grand Challenge," grand-challenge.org. https://luna16.grand-challenge.org/ (accessed Dec. 13, 2021).

D. J. Bell, "COVID-19 | Radiology Reference Article," Radiopaedia. https://radiopaedia.org/articles/covid-19-4 (accessed Dec. 13, 2021).

L. Wang, Z. Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports, vol. 10, no. 1, Nov. 2020, Art. no. 19549. DOI: https://doi.org/10.1038/s41598-020-76550-z

T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103792. DOI: https://doi.org/10.1016/j.compbiomed.2020.103792

I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635–640, Jun. 2020. DOI: https://doi.org/10.1007/s13246-020-00865-4

A. Sharma, S. Rani, and D. Gupta, "Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases," International Journal of Biomedical Imaging, vol. 2020, Oct. 2020, Art. no. e8889023. DOI: https://doi.org/10.1155/2020/8889023

J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, "COVID-19 open source data sets: a comprehensive survey," Applied Intelligence, vol. 51, no. 3, pp. 1296–1325, Mar. 2021. DOI: https://doi.org/10.1007/s10489-020-01862-6

A. Signoroni et al., "BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset," Medical Image Analysis, vol. 71, Jul. 2021, Art. no. 102046. DOI: https://doi.org/10.1016/j.media.2021.102046

"COVID Live Update: 270,678,636 Cases and 5,325,414 Deaths from the Coronavirus - Worldometer." https://www.worldometers.info/coronavirus/ (accessed Dec. 13, 2021).

Datopian, "Novel Coronavirus 2019," DataHub. https://datahub.io/core/covid-19 (accessed Dec. 13, 2021).

B. Xu et al., "Epidemiological data from the COVID-19 outbreak, real-time case information," Scientific Data, vol. 7, no. 1, Mar. 2020, Art. no. 106. DOI: https://doi.org/10.1038/s41597-020-0448-0

B. Xu and M. U. G. Kraemer, "Open access epidemiological data from the COVID-19 outbreak," The Lancet. Infectious Diseases, vol. 20, no. 5, May 2020, Art. no. 534. DOI: https://doi.org/10.1016/S1473-3099(20)30119-5

A. J. Kucharski et al., "Early dynamics of transmission and control of COVID-19: a mathematical modelling study," The Lancet Infectious Diseases, vol. 20, no. 5, pp. 553–558, May 2020. DOI: https://doi.org/10.1016/S1473-3099(20)30144-4

C. McDermott, M. Lacki, B. Sainsbury, J. Henry, M. Filippov, and C. Rossa, "Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound," Frontiers in Big Data, vol. 4, Mar. 2021, Art. no. 612561. DOI: https://doi.org/10.3389/fdata.2021.612561

"covid19_ultrasound/data at master • jannisborn/covid19_ultrasound," GitHub. https://github.com/jannisborn/covid19_ultrasound (accessed Dec. 13, 2021).

"Project Coswara | IISc." https://coswara.iisc.ac.in (accessed Dec. 13, 2021).

M. Pahar, M. Klopper, R. Warren, and T. Niesler, "COVID-19 cough classification using machine learning and global smartphone recordings," Computers in Biology and Medicine, vol. 135, Aug. 2021, Art. no. 104572. DOI: https://doi.org/10.1016/j.compbiomed.2021.104572

N. El-Rashidy et al., "Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic," Diagnostics, vol. 11, no. 7, Jul. 2021, Art. no. 1155. DOI: https://doi.org/10.3390/diagnostics11071155

E. Ong, M. U. Wong, A. Huffman, and Y. He, "COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning," Frontiers in Immunology, vol. 11, 2020, Art. no. 1581. DOI: https://doi.org/10.3389/fimmu.2020.01581

S. Deoras, "How ML Is Assisting In Development Of Covid-19 Vaccines," Analytics India Magazine, Jul. 28, 2020. https://analyticsindiamag.com/how-ml-is-assisting-in-development-of-covid-19-vaccines/ (accessed Dec. 13, 2021).

M. S. Rahman et al., "Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach," PeerJ, vol. 8, Jul. 2020, Art. no. e9572. DOI: https://doi.org/10.7717/peerj.9572

M. Prachar et al., "COVID-19 Vaccine Candidates: Prediction and Validation of 174 SARS-CoV-2 Epitopes," Mar. 2020. DOI: https://doi.org/10.1101/2020.03.20.000794

H. A. Owida, A. Al-Ghraibah, and M. Altayeb, "Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7296–7301, Aug. 2021. DOI: https://doi.org/10.48084/etasr.4123

"AlphaFold: Using AI for scientific discovery," Deepmind. https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery (accessed Dec. 13, 2021).

"Scientists Use Cloud-Based Supercomputing and AI to Develop COVID-19 Treatments and Vaccine Models," Hospimedica.com, Sep. 16, 2020. https://www.hospimedica.com/covid-19/articles/294784537/scientists-use-cloud-based-supercomputing-and-ai-to-develop-covid-19-treatments-and-vaccine-models.html (accessed Dec. 13, 2021).

N. Wang, Y. Fu, H. Zhang, and H. Shi, "An evaluation of mathematical models for the outbreak of COVID-19," Precision Clinical Medicine, vol. 3, no. 2, pp. 85–93, Jun. 2020. DOI: https://doi.org/10.1093/pcmedi/pbaa016

M. Liu, R. Thomadsen, and S. Yao, "Forecasting the spread of COVID-19 under different reopening strategies," Scientific Reports, vol. 10, no. 1, Nov. 2020, Art. no. 20367. DOI: https://doi.org/10.1038/s41598-020-77292-8

M.-T. Li et al., "Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases," Mathematical biosciences and engineering: MBE, vol. 17, no. 4, pp. 3710–3720, May 2020. DOI: https://doi.org/10.3934/mbe.2020208

A. R. Ives and C. Bozzuto, "Estimating and explaining the spread of COVID-19 at the county level in the USA," Communications Biology, vol. 4, no. 1, pp. 1–9, Jan. 2021. DOI: https://doi.org/10.1038/s42003-020-01609-6

M. A. Ibrahim and A. Al-Najafi, "Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt," Processes, vol. 8, no. 11, Nov. 2020, Art. no. 1400. DOI: https://doi.org/10.3390/pr8111400

T. Kuniya, "Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020," Journal of Clinical Medicine, vol. 9, no. 3, Mar. 2020, Art. no. 789. DOI: https://doi.org/10.3390/jcm9030789

T.-M. Chen, J. Rui, Q.-P. Wang, Z.-Y. Zhao, J.-A. Cui, and L. Yin, "A mathematical model for simulating the phase-based transmissibility of a novel coronavirus," Infectious Diseases of Poverty, vol. 9, no. 1, Feb. Art. no. 24, 2020. DOI: https://doi.org/10.1186/s40249-020-00640-3

P. Boldog, T. Tekeli, Z. Vizi, A. Denes, F. A. Bartha, and G. Rost, "Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China," Journal of Clinical Medicine, vol. 9, no. 2, Feb. 2020, Art. no. 571. DOI: https://doi.org/10.3390/jcm9020571

J. Hilton and M. J. Keeling, "Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices," PLOS Computational Biology, vol. 16, no. 7, 2020, Art. no. e1008031. DOI: https://doi.org/10.1371/journal.pcbi.1008031

S. Y. Lee, B. Lei, and B. Mallick, "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, vol. 15, no. 7, 2020, Art. no. e0236860. DOI: https://doi.org/10.1371/journal.pone.0236860

Y. Wang et al., "Unobtrusive and Automatic Classification of Multiple People’s Abnormal Respiratory Patterns in Real Time Using Deep Neural Network and Depth Camera," IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8559–8571, Sep. 2020. DOI: https://doi.org/10.1109/JIOT.2020.2991456

C. T. Rentsch et al., "Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study," PLOS Medicine, vol. 17, no. 9, 2020, Art. no. e1003379. DOI: https://doi.org/10.1371/journal.pmed.1003379

V. K. R. Chimmula and L. Zhang, "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, vol. 135, Jun. 2020, Art. no. 109864. DOI: https://doi.org/10.1016/j.chaos.2020.109864

S. Dutta and S. K. Bandyopadhyay, "Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Releas." 2020. DOI: https://doi.org/10.1101/2020.03.25.20043505

V. Kumar Singh, M. Abdel-Nasser, N. Pandey, and D. Puig, "LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework," Diagnostics, vol. 11, no. 2, Feb. 2021, Art. no. 158. DOI: https://doi.org/10.3390/diagnostics11020158

M. Zietz, J. Zucker, and N. P. Tatonetti, "Associations between blood type and COVID-19 infection, intubation, and death," Nature Communications, vol. 11, no. 1, Nov. 2020, Art. no. 5761. DOI: https://doi.org/10.1038/s41467-020-19623-x

V. Lopez and M. Cukic, "A dynamical model of SARS-CoV-2 based on people flow networks," Safety Science, vol. 134, Feb. 2021, Art. no. 105034. DOI: https://doi.org/10.1016/j.ssci.2020.105034

A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, "Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning," Journal of Biomolecular Structure and Dynamics, vol. 39, no. 15, pp. 5682–5689, Oct. 2021. DOI: https://doi.org/10.1080/07391102.2020.1788642

S. Ahuja, B. K. Panigrahi, N. Dey, V. Rajinikanth, and T. K. Gandhi, "Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices," Applied Intelligence, vol. 51, no. 1, pp. 571–585, Jan. 2021. DOI: https://doi.org/10.1007/s10489-020-01826-w

A. Sedik et al., "Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections," Viruses, vol. 12, no. 7, Jul. 2020, Art. no. 769. DOI: https://doi.org/10.3390/v12070769

B. Wang et al., "AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system," Applied Soft Computing, vol. 98, Jan. 2021, Art. no 106897. DOI: https://doi.org/10.1016/j.asoc.2020.106897

S. Wang et al., "A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)," European Radiology, vol. 31, no. 8, pp. 6096–6104, Aug. 2021. DOI: https://doi.org/10.1007/s00330-021-07715-1

D. Dansana et al., "Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm," Soft Computing, Aug. 2020. DOI: https://doi.org/10.1007/s00500-020-05275-y

F. Shi et al., "Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification," Physics in Medicine & Biology, vol. 66, no. 6, Mar. 2021, Art. no. 065031. DOI: https://doi.org/10.1088/1361-6560/abe838

P. K. Sethy, S. K. Behera, P. K. Ratha, and P. Biswas, "Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine," International Journal of Mathematical Engineering and Management Sciences, vol. 5, no. 4, pp. 643–651, 2020. DOI: https://doi.org/10.33889/IJMEMS.2020.5.4.052

C. Brown et al., "Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data," in 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, USA, Aug. 2020, pp. 3474–3484. DOI: https://doi.org/10.1145/3394486.3412865

M. Faezipour and A. Abuzneid, "Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds," Telemedicine journal and e-health, vol. 26, no. 10, pp. 1202–1205, Oct. 2020. DOI: https://doi.org/10.1089/tmj.2020.0114

N. Bung, S. R. Krishnan, G. Bulusu, and A. Roy, "De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence," Future Medicinal Chemistry, vol. 13, no. 6, pp. 575–585, Mar. 2021. DOI: https://doi.org/10.4155/fmc-2020-0262

K. Avchaciov, O. Burmistrova, and P. Fedichev, AI for the repurposing of approved or investigational drugs against COVID-19. 2020.

P. Richardson et al., "Baricitinib as potential treatment for 2019-nCoV acute respiratory disease," Lancet, vol. 395, no. 10223, pp. e30–e31, 2020. DOI: https://doi.org/10.1016/S0140-6736(20)30304-4

J. Fauqueur, A. Thillaisundaram, and T. Togia, "Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns," arXiv:1907.01417 [cs], Jul. 2019, Accessed: Dec. 13, 2021. [Online]. Available: http://arxiv.org/abs/1907.01417. DOI: https://doi.org/10.18653/v1/W19-5016

Y. Ge et al., "A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19," Mar. 2020. DOI: https://doi.org/10.1101/2020.03.11.986836

X. Zeng et al., "Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning," Journal of Proteome Research, vol. 19, no. 11, pp. 4624–4636, Nov. 2020. DOI: https://doi.org/10.1021/acs.jproteome.0c00316

C. Loucera, M. Esteban-Medina, K. Rian, M. M. Falco, J. Dopazo, and M. Pena-Chilet, "Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection," Signal Transduction and Targeted Therapy, vol. 5, Dec. 2020, Art. no. 290. DOI: https://doi.org/10.1038/s41392-020-00417-y

K. Hsieh et al., "Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation," Sep. 2020. Accessed: Dec. 13, 2021. [Online]. Available: https://ui.adsabs.harvard.edu/abs/2020arXiv200910931H. DOI: https://doi.org/10.21203/rs.3.rs-114758/v1

A. Zhavoronkov, "Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry," Molecular Pharmaceutics, vol. 15, no. 10, pp. 4311–4313, Oct. 2018. DOI: https://doi.org/10.1021/acs.molpharmaceut.8b00930

R. Batra, H. Chan, G. Kamath, R. Ramprasad, M. J. Cherukara, and S. K. R. S. Sankaranarayanan, "Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies," The Journal of Physical Chemistry Letters, vol. 11, no. 17, pp. 7058–7065, Sep. 2020. DOI: https://doi.org/10.1021/acs.jpclett.0c02278

R. Mall, A. Elbasir, H. A. Meer, S. Chawla, and E. Ullah, "Data-Driven Drug Repurposing for COVID-19," Jul. 2020. DOI: https://doi.org/10.26434/chemrxiv.12661103

B. Tang, F. He, D. Liu, M. Fang, Z. Wu, and D. Xu, "AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2," Mar. 2020. DOI: https://doi.org/10.1101/2020.03.03.972133

B. Trstenjak, D. Donko, and Z. Avdagic, "Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1212–1216, Dec. 2016. DOI: https://doi.org/10.48084/etasr.753

K. Koklonis, M. Sarafidis, M. Vastardi, and D. Koutsouris, "Utilization of Machine Learning in Supporting Occupational Safety and Health Decisions in Hospital Workplace," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7262–7272, Jun. 2021. DOI: https://doi.org/10.48084/etasr.4205

Downloads

How to Cite

[1]
S. A. A. Biabani and N. A. Tayyib, “A Review on the Use of Machine Learning Against the Covid-19 Pandemic”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 8039–8044, Feb. 2022.

Metrics

Abstract Views: 424
PDF Downloads: 267

Metrics Information
Bookmark and Share