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Machine Learning and COVID-19: Lessons from SARS-CoV-2

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Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 1412))

Abstract

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

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References

  1. Epidemiology Working Group for NCIP Epidemic Response, Chinese Center for Disease Control and Prevention (2020) [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 41(2):145–151

    Google Scholar 

  2. Awasthi A, Vishwas S, Corrie L, et al (2020) OUTBREAK of novel corona virus disease (COVID-19): Antecedence and aftermath. Eur J Pharmacol 884:173381. https://doi.org/10.1016/j.ejphar.2020.173381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Manigandan S, Wu MT, Ponnusamy VK, Raghavendra VB, et al (2020) A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19. Process Biochem 98:233–240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Huang C, Wang Y, Li X, et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 (10223):497–506

    Article  CAS  PubMed  Google Scholar 

  5. Chen N, Zhou M, Dong X, et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223):507–513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Centers for Disease Control and Prevention; Symptoms of COVID-19; 11 Aug 2022. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. Accessed September 13, 2022 Available: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html

  7. Wiersinga WJ, Rhodes A, Cheng AC, et al (2020) Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA. 324(8):782–793

    Article  CAS  PubMed  Google Scholar 

  8. Jamison DA Jr, Anand Narayanan S, Trovão NS, et al (2022) A comprehensive SARS-CoV-2 and COVID-19 review, Part 1: Intracellular overdrive for SARS-CoV-2 infection. Eur J Hum Genet 30(8):889–898

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lamers MM, Haagmans BL (2020) SARS-CoV-2 pathogenesis. Nat Rev Microbiol 20(5):270–284

    Article  Google Scholar 

  10. Triggle CR, Bansal D, Ding H, et al (2021) A comprehensive review of viral characteristics, transmission, pathophysiology, immune response, and management of SARS-CoV-2 and COVID-19 as a basis for controlling the pandemic. Front Immunol 12:631139. https://doi.org/10.3389/fimmu.2021.631139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. da Silva Torres MK, Bichara CDA, de Almeida M de N do S, et al (2022) The Complexity of SARS-CoV-2 Infection and the COVID-19 Pandemic. Front Microbiol 13:789882. https://doi.org/10.3389/fmicb.2022.789882

  12. Watson OJ, Barnsley G, Toor J, et al (2022) Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis 22(9):1293–1302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bullock J, Lane JE, Shults FL (2022) What causes COVID-19 vaccine hesitancy? Ignorance and the lack of bliss in the United Kingdom. Humanities and Social Sciences Communications 9(87):1–7. https://doi.org/10.1057/s41599-022-01092-w

    Article  Google Scholar 

  14. Lazarus JV, Wyka K, White TM, et al (2021) Revisiting COVID-19 vaccine hesitancy around the world using data from 23 countries in 2021. Nat Commun 13(1):3801. https://doi.org/10.1038/s41467-022-31441-x

    Article  CAS  Google Scholar 

  15. Troiano G, Nardi A (2021) Vaccine hesitancy in the era of COVID-19. Public Health 194:245–251

    Article  CAS  PubMed  Google Scholar 

  16. Avila-Ponce de León U, Pérez ÁGC, Avila-Vales E (2020) An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast. Chaos Solitons Fractals 140:110165. https://doi.org/10.1016/j.chaos.2020.110165

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ngonghala CN, Knitter JR, Marinacci L, et al (2021) Assessing the impact of widespread respirator use in curtailing COVID-19 transmission in the USA. R Soc Open Sci 8(9):210699. https://doi.org/10.1098/rsos.210699.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sadria M, Layton AT (2021) Modeling within-Host SARS-CoV-2 Infection Dynamics and Potential Treatments. Viruses 13(6):1141. https://doi.org/10.3390/v13061141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vaidya NK, Bloomquist A, Perelson AS (2021) Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets. Viruses 13(8):1635. https://doi.org/10.3390/v13081635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li J, Wu J, Zhang J, et al (2022) A multicompartment mathematical model based on host immunity for dissecting COVID-19 heterogeneity. Heliyon 8(5):e09488. https://doi.org/10.1016/j.heliyon.2022.e09488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hernandez-Vargas EA, Velasco-Hernandez JX (2020) In-host Mathematical Modelling of COVID-19 in Humans. Annu Rev Control 50:448–456

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shen J, Zhang CJP, Jiang B, et al (2019) Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med Inform 7(3):e10010 https://doi.org/10.2196/10010

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hwang TJ, Kesselheim AS, Vokinger KN (2019) Lifecycle Regulation of Artificial Intelligence- and Machine Learning-Based Software Devices in Medicine. JAMA 322(23):2285–2286

    Article  PubMed  Google Scholar 

  24. Zheng Q, Jones FK, Leavitt SV, et al (2020) HIT-COVID, a global database tracking public health interventions to COVID-19. Sci Data 7(1):286. https://doi.org/10.1038/s41597-020-00610-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Desvars-Larrive A, Dervic E, Haug N, et al (2020) A structured open dataset of government interventions in response to COVID-19. Sci Data 7(1):285. https://doi.org/10.1038/s41597-020-00609-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mathieu E, Ritchie H, Ortiz-Ospina E, et al (2021) A global database of COVID-19 vaccinations. Nat Hum Behav 5(7):947–953

    Article  PubMed  Google Scholar 

  27. Hale T, Angrist N, Goldszmidt R, et al (2021) A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav 5(4):529–538

    Article  PubMed  Google Scholar 

  28. Dong E, Du H, Gardner L (2020) An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 20(5):533–534

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Elbe S, Buckland-Merrett G (2017) Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall 1(1):33–46

    Article  PubMed  PubMed Central  Google Scholar 

  30. Rockett RJ, Arnott A, Lam C, et al (2020) Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Nat Med 26(9):1398–1404

    Article  CAS  PubMed  Google Scholar 

  31. Obermeyer F, Jankowiak M, Barkas N, et al (2022) Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness. Science 376(6599):1327–1332

    Article  CAS  PubMed  Google Scholar 

  32. Liao M, Liu Y, Yuan J, et al (2020) Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med 26(6):842–844

    Article  CAS  PubMed  Google Scholar 

  33. Bost P, Giladi A, Liu Y, et al (2020) Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients. Cell 181(7):1475–1488.e12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Valyaeva AA, Zharikova AA, Kasianov AS, et al (2020) Expression of SARS-CoV-2 entry factors in lung epithelial stem cells and its potential implications for COVID-19. Sci Rep 10(1):17772. https://doi.org/10.1038/s41598-020-74598-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhu L, Yang P, Zhao Y, et al (2020) Single-Cell Sequencing of Peripheral Mononuclear Cells Reveals Distinct Immune Response Landscapes of COVID-19 and Influenza Patients. Immunity 53(3):685–696.e3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Garg M, Li X, Moreno P, et al (2021) Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses. Sci Rep 11(1):20833. https://doi.org/10.1038/s41598-021-00121-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jeong H-H, Jia J, Dai Y, et al (2021) Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches. Genes (Basel) 12(5):635. https://doi.org/10.3390/genes12050635

    Article  CAS  PubMed  Google Scholar 

  38. Vázquez-Jiménez A, Avila-Ponce De León UE, Matadamas-Guzman M, et al (2021) On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers. Front Immunol 12:705646. https://doi.org/10.3389/fimmu.2021.705646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gracia Villacampa E, Larsson L, Mirzazadeh R, et al (2021) Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 8:100065. https://doi.org/10.1016/j.xgen.2021.100065

    Article  CAS  Google Scholar 

  40. Liebal UW, Phan ANT, Sudhakar M, et al (2020) Machine Learning Applications for Mass Spectrometry-Based Metabolomics. Metabolites 10(6):243. https://doi.org/10.3390/metabo10060243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hasan MR, Suleiman M, Pérez-López A (2021) Metabolomics in the Diagnosis and Prognosis of COVID-19. Front Genet 12:721556. https://doi.org/10.3389/fgene.2021.721556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Giovannini G, Haick H, Garoli D (2021) Detecting COVID-19 from Breath: A Game Changer for a Big Challenge. ACS Sens 6(4):1408–1417

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nachtigall FM, Pereira A, Trofymchuk OS, Santos LS (2020) Author Correction: Detection of SARS-CoV-2 in nasal swabs using MALDI-MS. Nat Biotechnol 38(10):1211. https://doi.org/10.1038/s41587-020-0701-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Grassin-Delyle S, Roquencourt C, Moine P, et al (2021) Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study. EBioMedicine 63:103154. https://doi.org/10.1016/j.ebiom.2020.103154

    Article  CAS  PubMed  Google Scholar 

  45. Bennet S, Kaufmann M, Takami K, et al (2022) Small-molecule metabolome identifies potential therapeutic targets against COVID-19. Sci Rep 12(1):10029. https://doi.org/10.1038/s41598-022-14050-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Valdés A, Moreno LO, Rello SR, et al (2022) Metabolomics study of COVID-19 patients in four different clinical stages. Sci Rep 12(1):1650. https://doi.org/10.1038/s41598-022-05667-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sindelar M, Stancliffe E, Schwaiger-Haber M, et al (2021) Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity. medRxiv 2021.02.05.21251173. https://doi.org/10.1101/2021.02.05.21251173

  48. Richard VR, Gaither C, Popp R, et al (2022) Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning. Mol Cell Proteomics 21(10):100277. https://doi.org/10.1016/j.mcpro.2022.100277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Roberts I, Wright Muelas M, Taylor JM, et al (2021) Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome. Metabolomics 18(1):6. https://doi.org/10.1007/s11306-021-01859-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jia H, Liu C, Li D, et al (2022) Metabolomic analyses reveal new stage-specific features of COVID-19. Eur Respir J 59(2):2100284. https://doi.org/10.1183/13993003.00284-2021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. de Fátima Cobre A, Surek M, Stremel DP, et al (2022) Diagnosis and prognosis of COVID-19 employing analysis of patients’ plasma and serum via LC-MS and machine learning. Comput Biol Med 146:105659. https://doi.org/10.1016/j.compbiomed.2022.105659

    Article  CAS  Google Scholar 

  52. Sen P, Lamichhane S, Mathema VB, et al (2021) Deep learning meets metabolomics: a methodological perspective. Brief Bioinform 22(2):1531–1542

    Article  PubMed  Google Scholar 

  53. Bhuyan HK, Chakraborty C, Shelke Y, Pani SK (2022) COVID-19 diagnosis system by deep learning approaches. Expert Syst 39(3):e12776. https://doi.org/10.1111/exsy.12776

    Article  PubMed  Google Scholar 

  54. Byeon SK, Madugundu AK, Garapati K, et al (2022) Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study. Lancet Digit Health 4(9):e632–e645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Erickson N, Mueller J, Shirkov A, et al (2020) AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv:2003.06505. Preprint. https://doi.org/10.48550/arXiv.2003.06505

  56. Papoutsoglou G, Karaglani M, Lagani V, et al (2021) Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets. Sci Rep 11(1):15107. https://doi.org/10.1038/s41598-021-94501-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Marchesi JR, Ravel J (2015) The vocabulary of microbiome research: a proposal. Microbiome 3:31. https://doi.org/10.1186/s40168-015-0094-5

    Article  PubMed  PubMed Central  Google Scholar 

  58. Mohajeri MH, Brummer RJM, Rastall RA, et al (2018) The role of the microbiome for human health: from basic science to clinical applications. Eur J Nutr 57(Suppl 1):1–14

    Article  PubMed  PubMed Central  Google Scholar 

  59. Wu Y, Cheng X, Jiang G, et al (2021) Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization. NPJ Biofilms Microbiomes 7(1):61. https://doi.org/10.1038/s41522-021-00232-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kodikara S, Ellul S, Lê Cao KA (2022) Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 23(4):bbac273. https://doi.org/10.1093/bib/bbac273

  61. Topçuoğlu BD, Lesniak NA, Ruffin MT 4th, et al (2020) A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems. mBio 11(3):e00434-20. https://doi.org/10.1128/mBio.00434-20

  62. Durazzi F, Sala C, Castellani G, et al (2021) Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 11(1):3030. https://doi.org/10.1038/s41598-021-82726-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Liu YX, Qin Y, Chen T, et al (2021) A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 2(5):315–330

    Article  CAS  Google Scholar 

  64. Martínez-López YE, Esquivel-Hernández DA, Sánchez-Castañeda JP, et al (2022) Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era. Gut Microbes 4(1):2111952. https://doi.org/10.1080/19490976.2022.2111952

    Article  CAS  Google Scholar 

  65. Ke S, Weiss ST, Liu YY (2022) Dissecting the role of the human microbiome in COVID-19 via metagenome-assembled genomes. Nat Commun 13(1):5235. https://doi.org/10.1038/s41467-022-32991-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang F, Wan Y, Zuo T, et al (2022) Prolonged Impairment of Short-Chain Fatty Acid and L-Isoleucine Biosynthesis in Gut Microbiome in Patients With COVID-19. Gastroenterology 162(2):548–561.e4

    Article  CAS  PubMed  Google Scholar 

  67. Hirayama M, Nishiwaki H, Hamaguchi T, et al (2021) Intestinal Collinsella may mitigate infection and exacerbation of COVID-19 by producing ursodeoxycholate. PLoS One 16(11):e0260451. https://doi.org/10.1038/s41467-022-32991-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods. arXiv:2003.09424. https://doi.org/10.48550/arXiv.2003.09424

  69. Guhathakurata S, Kundu S, Chakraborty A, Banerjee JS (2021) Chapter 18 - A novel approach to predict COVID-19 using support vector machine. In: Data Science for COVID-19; Volume 1: Computational Perspectives; Kose U, Gupta D, de Albuquerque VHC, Khanna A (eds). Academic Press; Cambridge, MA, USA. pp 351–364. ISBN-13: 978-0128245361

    Google Scholar 

  70. Hu R, Gan J, Zhu X, et al (2022) Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data. Inf Process Manag 59(1):102782. https://doi.org/10.1016/j.ipm.2021.102782

    Article  PubMed  Google Scholar 

  71. Ong AKS, Chuenyindee T, Prasetyo YT, et al (2022) Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”. Int J Environ Res Public Health 19(10):6111. https://doi.org/10.3390/ijerph19106111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Moslehi S, Rabiei N, Soltanian AR, Mamani M (2022) Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran. BMC Med Inform Decis Mak 22(1):192. https://doi.org/10.1186/s12911-022-01939-x

    Article  PubMed  Google Scholar 

  73. Parbat D, Chakraborty M (2020) A python based support vector regression model for prediction of COVID19 cases in India. Chaos Solitons Fractals 138:109942. https://doi.org/10.1016/j.chaos.2020.109942

    Article  PubMed  PubMed Central  Google Scholar 

  74. Yadav M, Perumal M, Srinivas M (2020) Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solitons Fractals 139:110050. https://doi.org/10.1016/j.chaos.2020.110050

    Article  PubMed  PubMed Central  Google Scholar 

  75. Ribeiro MHDM, da Silva RG, Mariani VC, Coelho LDS (2020) Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos Solitons Fractals 135:109853. https://doi.org/10.1016/j.chaos.2020.109853

    Article  PubMed  PubMed Central  Google Scholar 

  76. da Silva RG, Ribeiro MHDM, Mariani VC, Coelho LDS (2020) Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables. Chaos Solitons Fractals 139:110027. https://doi.org/10.1016/j.chaos.2020.110027

    Article  PubMed  PubMed Central  Google Scholar 

  77. Singh KK, Kumar S, Dixit P, Bajpai MK (2021) Kalman filter based short term prediction model for COVID-19 spread. Appl Intell (Dordr) 51(5):2714–2726

    Article  PubMed  Google Scholar 

  78. Galasso J, Cao DM, Hochberg R (2022) A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data. Chaos Solitons Fractals 156:111779. https://doi.org/10.1016/j.chaos.2021.111779

    Article  PubMed  PubMed Central  Google Scholar 

  79. Benvenuto D, Giovanetti M, Vassallo L, et al (2020) Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief 29:105340. https://doi.org/10.1016/j.dib.2020.105340

    Article  PubMed  PubMed Central  Google Scholar 

  80. Ceylan Z (2020) Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ 729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sahai AK, Rath N, Sood V, Singh MP (2020) ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes Metab Syndr 14(5):1419–1427

    Article  PubMed  PubMed Central  Google Scholar 

  82. Alabdulrazzaq H, Alenezi MN, Rawajfih Y, et al (2021) On the accuracy of ARIMA based prediction of COVID-19 spread. Results Phys 27:104509. https://doi.org/10.1016/j.rinp.2021.104509

    Article  PubMed  PubMed Central  Google Scholar 

  83. Rguibi MA, Moussa N, Madani A, et al (2022) Forecasting Covid-19 Transmission with ARIMA and LSTM Techniques in Morocco. SN Comput Sci 3(2):133. https://doi.org/10.1007/s42979-022-01019-x

    Article  PubMed  PubMed Central  Google Scholar 

  84. Rahman MS, Chowdhury AH, Amrin M (2022) Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh. PLOS Global Public Health 2:e0000495. https://doi.org/10.1371/journal.pgph.0000495

    Article  PubMed  PubMed Central  Google Scholar 

  85. Gumel AB, Iboi EA, Ngonghala CN, Elbasha EH (2021) A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations. Infect Dis Model 6:148–168

    PubMed  Google Scholar 

  86. Saldaña F, Velasco-Hernández JX (2022) Modeling the COVID-19 pandemic: a primer and overview of mathematical epidemiology. SeMA Journal 79:225–251

    Article  Google Scholar 

  87. Yang Z, Zeng Z, Wang K, et al (2020) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 12(3):165–174

    Article  PubMed  PubMed Central  Google Scholar 

  88. Zisad SN, Hossain MS, Hossain MS, Andersson K (2021) An Integrated Neural Network and SEIR Model to Predict COVID-19. Algorithms 28(4):733–743

    Google Scholar 

  89. Huang T, Chu Y, Shams S, et al (2021) Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates. J Biomed Inform 119:103818. https://doi.org/10.1016/j.jbi.2021.103818

    Article  PubMed  PubMed Central  Google Scholar 

  90. Zou D, Wang L, Xu P, et al (2020) Epidemic model guided machine learning for COVID-19 forecasts in the United States. medRxiv. https://doi.org/10.1101/2020.05.24.20111989

  91. Zheng N, Du S, Wang J, et al (2020) Predicting COVID-19 in China Using Hybrid AI Model. IEEE Trans Cybern 50(7):2891–2904

    Article  PubMed  Google Scholar 

  92. Delli Compagni R, Cheng Z, Russo S, Van Boeckel TP (2022) A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics. PLoS One 17(3):e0263789. https://doi.org/10.1371/journal.pone.0263789

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Dandekar R, Rackauckas C, Barbastathis G (2020) A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread. Patterns (N Y) 1(9):100145. https://doi.org/10.1016/j.patter.2020.100145

    Article  CAS  PubMed  Google Scholar 

  94. Ala’raj M, Majdalawieh M, Nizamuddin N (2021) Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infect Dis Model 6:98–111

    PubMed  Google Scholar 

  95. Rahmadani F, Lee H (2020) Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case. NATO Adv Sci Inst Ser E Appl Sci 10(23):8539. https://doi.org/10.3390/app10238539

    Article  CAS  Google Scholar 

  96. Dong D, Tang Z, Wang S, et al (2021) The Role of Imaging in the Detection and Management of COVID-19: A Review. IEEE Rev Biomed Eng 14:16–29

    Article  PubMed  Google Scholar 

  97. Liszewski MC, Görkem S, Sodhi KS, Lee EY (2017) Lung magnetic resonance imaging for pneumonia in children. Pediatr Radiol 47(11):1420–1430

    Article  PubMed  Google Scholar 

  98. Silva P, Luz E, Silva G, et al (2020) COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis. Inform Med Unlocked 20:100427. https://doi.org/10.1016/j.imu.2020.100427

    Article  PubMed  PubMed Central  Google Scholar 

  99. Jadon S (2021) COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach. Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications. arXiv:2102.06285. https://doi.org/10.1117/12.2581496

  100. Nayak SR, Nayak DR, Sinha U, et al (2021) Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomed Signal Process Control 64:102365. https://doi.org/10.1016/j.bspc.2020.102365

    Article  PubMed  Google Scholar 

  101. Waheed A, Goyal M, Gupta D, et al (2020) CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection. IEEE Access 8:91916–91923

    Article  PubMed  Google Scholar 

  102. Chowdhury MEH, Rahman T, Khandakar A, et al (2020) Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access 2020:132665–132676. https://doi.org/10.1109/access.2020.3010287

    Article  Google Scholar 

  103. Khan S, Mukati A (2020) Dataset Augmentation for Machine Learning Applications of Dental Radiography. International Journal of Advanced Computer Science and Applications 11(2). https://doi.org/10.14569/ijacsa.2020.0110258

  104. Li J, Zhang D, Liu Q, et al (2020) COVID-GATNet: A Deep Learning Framework for Screening of COVID-19 from Chest X-Ray Images. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). pp 1897–1902. https://doi.org/10.1109/iccc51575.2020.9345005

  105. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 770–778. https://doi.org/10.1109/cvpr.2016.90

  106. Howard AG, Zhu M, Chen B, et al (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861v1. https://doi.org/10.48550/ARXIV.1704.04861

  107. Szegedy C, Vanhoucke V, Ioffe S, et al (2016) Rethinking the Inception Architecture for Computer Vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pp 2818–2826. https://doi.org/10.1109/cvpr.2016.308

  108. Mary Shyni H, Chitra E (2022) A COMPARATIVE STUDY OF X-RAY AND CT IMAGES IN COVID-19 DETECTION USING IMAGE PROCESSING AND DEEP LEARNING TECHNIQUES. Comput Methods Programs Biomed Update 2:100054. https://doi.org/10.1016/j.cmpbup.2022.100054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Ahsan MM, Ahad MT, Soma FA, et al (2021) Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence. IEEE Access 9:35501–35513

    Article  PubMed  Google Scholar 

  110. El-Kenawy ESM, Mirjalili S, Ibrahim A, et al (2021) Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification. IEEE Access 9:36019–36037

    Article  PubMed  Google Scholar 

  111. Mahmud T, Alam MJ, Chowdhury S, et al (2021) CovTANet: A Hybrid Tri-Level Attention-Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans. IEEE Transactions on Industrial Informatics. 2021. pp. 6489–6498. https://doi.org/10.48550/arXiv.2101.00691

  112. Li H, Huang F, Liao H, et al (2022) Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method. Front Mol Biosci 9:952626. https://doi.org/10.3389/fmolb.2022.952626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Chen L, Mei Z, Guo W, et al (2022) Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods. Biomed Res Int 2022:6089242. https://doi.org/10.1155/2022/6089242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Amrute JM, Perry AM, Anand G, et al (2022) Cell specific peripheral immune responses predict survival in critical COVID-19 patients. Nat Commun 13(1):882. https://doi.org/10.1038/s41467-022-28505-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Patterson BK, Guevara-Coto J, Yogendra R, et al (2021) Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning. Front Immunol 12:700782. https://doi.org/10.3389/fimmu.2021.700782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Mueller YM, Schrama TJ, Ruijten R, et al (2021) Immunophenotyping and machine learning identify distinct immunotypes that predict COVID-19 clinical severity. bioRxiv. medRxiv. https://doi.org/10.1101/2021.05.07.21256531

  117. Zhang S, Cooper-Knock J, Weimer AK, et al (2022) Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity. Cell Syst 13(8):598–614.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Lee JW, Su Y, Baloni P, et al (2022) Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19. Nat Biotechnol 40(1):110–120

    Article  CAS  PubMed  Google Scholar 

  119. Diener C, Gibbons SM, Resendis-Antonio O. MICOM (2020): Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. mSystems 5(1):e00606–19.

    Google Scholar 

  120. Hale VL, Jeraldo P, Mundy M, et al (2018) Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods 149:59–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Su Q, Liu Q, Lau RI, et al (2022) Faecal microbiome-based machine learning for multi-class disease diagnosis. Nat Commun 13(1):6818. https://doi.org/10.1038/s41467-022-34405-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

    Ugo Avila-Ponce de León

  2. Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico

    Ugo Avila-Ponce de León, Aarón Vazquez-Jimenez, Galilea Resendis-González, Daniel Neri-Rosario & Osbaldo Resendis-Antonio

  3. Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico

    Alejandra Cervera

  4. Coordinación de la Investigación Científica – Red de Apoyo a la Investigación – Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico

    Osbaldo Resendis-Antonio

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  1. Ugo Avila-Ponce de León
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  2. Aarón Vazquez-Jimenez
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  3. Alejandra Cervera
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  4. Galilea Resendis-González
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  1. University of Campinas, Campinas, São Paulo, Brazil

    Paul C. Guest

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Avila-Ponce de León, U., Vazquez-Jimenez, A., Cervera, A., Resendis-González, G., Neri-Rosario, D., Resendis-Antonio, O. (2023). Machine Learning and COVID-19: Lessons from SARS-CoV-2. In: Guest , P.C. (eds) Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19. Advances in Experimental Medicine and Biology(), vol 1412. Springer, Cham. https://doi.org/10.1007/978-3-031-28012-2_17

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