{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T14:44:35Z","timestamp":1746715475479,"version":"3.40.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s13198-022-01735-w","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T06:02:50Z","timestamp":1660888970000},"page":"346-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts"],"prefix":"10.1007","volume":"15","author":[{"given":"Amalraj","family":"Irudayasamy","sequence":"first","affiliation":[]},{"given":"D.","family":"Ganesh","sequence":"additional","affiliation":[]},{"given":"M.","family":"Natesh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1255-9621","authenticated-orcid":false,"given":"N.","family":"Rajesh","sequence":"additional","affiliation":[]},{"given":"Umi","family":"Salma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"issue":"1","key":"1735_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s42256-020-00254-2","volume":"3","author":"M Barish","year":"2020","unstructured":"Barish M, Bolourani S, Lau LF, Shah S, Zanos TP (2020) External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19. Nat Mach Intell 3(1):25\u201327. https:\/\/doi.org\/10.1038\/s42256-020-00254-2","journal-title":"Nat Mach Intell"},{"issue":"10","key":"1735_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0241332","volume":"15","author":"JJ Bird","year":"2020","unstructured":"Bird JJ, Barnes CM, Premebida C, Ek\u00e1rt A, Faria DR (2020) Country-level pandemic risk and preparedness classification based on COVID-19 data: a machine learning approach. PLoS ONE 15(10):e0241332. https:\/\/doi.org\/10.1371\/journal.pone.0241332","journal-title":"PLoS ONE"},{"key":"1735_CR3","unstructured":"Cascella M, Rajnik M, Aleem A, et al. Features, evaluation, and treatment of coronavirus (COVID-19) [Updated 2022 May 4]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK554776\/"},{"key":"1735_CR4","unstructured":"European Centre for Disease Prevention and Control (ECDC), 2021b. Weekly epidemiological update: omicron variant of concern (VOC) \u2013 Week 50 (data as of 19 December 2021b). https:\/\/www.ecdc.europa.eu\/en\/news-events\/weekly-epide miological-update-omicron-variant-concern-voc-week-50-data-19-december-2021b. (Accessed 5 Jan 2022)"},{"key":"1735_CR5","unstructured":"European Centre for Disease Prevention and Control (ECDC), 2021a. Threat Assessment brief: implications of the emergence and spread of the SARS-CoV-2 B.1.1. 529 Variant of concern (Omicron) for the EU\/EEA. https:\/\/www.ecdc.europa.eu\/en \/publications-data\/threat-assessment-brief-emergence-sars-cov-2-variant-b.1.1.529. (Accessed 5 Jan. 2022)"},{"key":"1735_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12475","author":"N Imtiaz Khan","year":"2021","unstructured":"Imtiaz Khan N, Mahmud T, Nazrul Islam M (2021) COVID-19 and black fungus: analysis of the public perceptions through machine learning. Eng Rep. https:\/\/doi.org\/10.1002\/eng2.12475","journal-title":"Eng Rep"},{"key":"1735_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(21)02758-6","author":"SSA Karim","year":"2021","unstructured":"Karim SSA, Karim QA (2021) Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet. https:\/\/doi.org\/10.1016\/s0140-6736(21)02758-6","journal-title":"Lancet"},{"key":"1735_CR8","doi-asserted-by":"publisher","DOI":"10.13052\/jmm1550-4646.1829","author":"VV Kumar","year":"2021","unstructured":"Kumar VV, Raghunath KMK, Rajesh N, Venkatesan M, Joseph RB, Thillaiarasu N (2021) Paddy plant disease recognition, risk analysis, and classification using deep convolution neuro-fuzzy network. J Mob Multimed. https:\/\/doi.org\/10.13052\/jmm1550-4646.1829","journal-title":"J Mob Multimed"},{"key":"1735_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17280-8","author":"W Liang","year":"2020","unstructured":"Liang W, Yao J, Chen A, Lv Q, Zanin M, Liu J, He J (2020) Early triage of critically ill COVID-19 patients using deep learning. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-020-17280-8","journal-title":"Nat Commun"},{"key":"1735_CR10","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jchromb.2012.05.020","volume":"910","author":"X Lin","year":"2012","unstructured":"Lin X, Yang F, Zhou L, Yin P, Kong H, Xing W, Lu X, Jia L, Wang Q, Xu G (2012) A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J Chromatogr B 910:149\u2013155. https:\/\/doi.org\/10.1016\/j.jchromb.2012.05.020","journal-title":"J Chromatogr B"},{"key":"1735_CR11","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciab1072","author":"T Maruki","year":"2022","unstructured":"Maruki T, Iwamoto N, Kanda K, Okumura N, Yamada G, Ishikane M, Ujiie M, Saito M, Fujimoto T, Kageyama T, Saito T, Saito S, Suzuki T, Ohmagari N (2022) Two cases of breakthrough SARS-CoV-2 infections caused by the Omicron variant (B.1.1.529 lineage) in international travelers to Japan. Clin Infect Dis. https:\/\/doi.org\/10.1093\/cid\/ciab1072","journal-title":"Clin Infect Dis"},{"issue":"7890","key":"1735_CR12","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1038\/d41586-021-03698-7","volume":"600","author":"A Maxmen","year":"2021","unstructured":"Maxmen A (2021) Omicron blindspots: why it\u2019s hard to track coronavirus variants. Nature 600(7890):579\u2013579. https:\/\/doi.org\/10.1038\/d41586-021-03698-7","journal-title":"Nature"},{"issue":"8","key":"1735_CR13","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1038\/s41591-020-0931-3","volume":"26","author":"X Mei","year":"2020","unstructured":"Mei X, Lee H-C, Diao K, Huang M, Lin B, Liu C, Yang Y (2020) Artificial intelligence\u2013enabled rapid diagnosis of patients with COVID-19. Nat Med 26(8):1224\u20131228. https:\/\/doi.org\/10.1038\/s41591-020-0931-3","journal-title":"Nat Med"},{"key":"1735_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.27561","author":"RK Mohapatra","year":"2021","unstructured":"Mohapatra RK, Sarangi AK, Kandi V, Azam M, Tiwari R, Dhama K (2021) Omicron (B.1.1.529 variant of SARS-CoV-2); an emerging threat: current global scenario. J Med Virol. https:\/\/doi.org\/10.1002\/jmv.27561","journal-title":"J Med Virol"},{"key":"1735_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-13993-7","author":"J-M Oh","year":"2020","unstructured":"Oh J-M, Venters CC, Di C, Pinto AM, Wan L, Younis I, Cai Z, Arai C, So BR, Duan J, Dreyfuss G (2020) U1 snRNP regulates cancer cell migration and invasion in vitro. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-019-13993-7","journal-title":"Nat Commun"},{"key":"1735_CR16","unstructured":"Omicron daily cases by country (COVID-19 variant). (n.d.). Kaggle.com. https:\/\/www.kaggle.com\/yamqwe\/omicron-covid19-variant-daily-cases"},{"issue":"10","key":"1735_CR17","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.3390\/ijerph19106111","volume":"19","author":"A Ong","year":"2022","unstructured":"Ong A, Chuenyindee T, Prasetyo YT, Nadlifatin R, Persada SF, Gumasing M, German JD, Robas K, Young MN, Sittiwatethanasiri T (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 \u201cThaiChana.\u201d Int J Environ Res Public Health 19(10):6111. https:\/\/doi.org\/10.3390\/ijerph19106111","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"1735_CR18","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"L Peterson","year":"2009","unstructured":"Peterson L (2009) K-Nearest Neighbor. Scholarpedia 4(2):1883. https:\/\/doi.org\/10.4249\/scholarpedia.1883","journal-title":"Scholarpedia"},{"key":"1735_CR19","doi-asserted-by":"publisher","DOI":"10.1101\/2021.11.11.21266068","author":"JRC Pulliam","year":"2021","unstructured":"Pulliam JRC, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H (2021) Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science. https:\/\/doi.org\/10.1101\/2021.11.11.21266068","journal-title":"Science"},{"issue":"11","key":"1735_CR20","doi-asserted-by":"publisher","first-page":"5846","DOI":"10.1109\/tii.2019.2912723","volume":"15","author":"S Salloum","year":"2019","unstructured":"Salloum S, Huang JZ, He Y (2019) Random sample partition: a distributed data model for big data analysis. IEEE Trans Industr Inf 15(11):5846\u20135854. https:\/\/doi.org\/10.1109\/tii.2019.2912723","journal-title":"IEEE Trans Industr Inf"},{"issue":"1","key":"1735_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/s41591-019-0727-5","volume":"26","author":"S Shilo","year":"2020","unstructured":"Shilo S, Rossman H, Segal E (2020) Axes of a revolution: challenges and promises of big data in healthcare. Nat Med 26(1):29\u201338. https:\/\/doi.org\/10.1038\/s41591-019-0727-5","journal-title":"Nat Med"},{"issue":"6","key":"1735_CR22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1016\/j.cell.2020.11.032","volume":"183","author":"AC Walls","year":"2020","unstructured":"Walls AC, Park Y-J, Tortorici MA, Wall A, McGuire AT, Veesler D (2020) Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 183(6):1735. https:\/\/doi.org\/10.1016\/j.cell.2020.11.032","journal-title":"Cell"},{"key":"1735_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110058","volume":"139","author":"P Wang","year":"2020","unstructured":"Wang P, Zheng X, Li J, Zhu B (2020) Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos, Solitons Fractals 139:110058. https:\/\/doi.org\/10.1016\/j.chaos.2020.110058","journal-title":"Chaos, Solitons Fractals"},{"key":"1735_CR24","unstructured":"World Health Organization (2021) Classification of Omicron (B.1.1.529): SARS-CoV-2 variant of concern. www.who.int. https:\/\/www.who.int\/news\/item\/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern"},{"key":"1735_CR25","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.2648","author":"Z Wu","year":"2020","unstructured":"Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA. https:\/\/doi.org\/10.1001\/jama.2020.2648","journal-title":"JAMA"},{"key":"1735_CR26","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m1328","author":"L Wynants","year":"2020","unstructured":"Wynants L, Van Calster B, Bonten MMJ, Collins GS, Debray TPA, De Vos M, Haller MC, Heinze G, Moons KGM, Riley RD, Schuit E, Smits LJM, Snell KIE, Steyerberg EW, Wallisch C, van Smeden M (2020) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. https:\/\/doi.org\/10.1136\/bmj.m1328","journal-title":"BMJ"},{"key":"1735_CR27","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2011.10.021","volume":"83","author":"W Yang","year":"2012","unstructured":"Yang W, Wang K, Zuo W (2012) Fast neighborhood component analysis. Neurocomputing 83:31\u201337. https:\/\/doi.org\/10.1016\/j.neucom.2011.10.021","journal-title":"Neurocomputing"},{"issue":"10","key":"1735_CR28","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","volume":"2","author":"K-H Yu","year":"2018","unstructured":"Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719\u2013731. https:\/\/doi.org\/10.1038\/s41551-018-0305-z","journal-title":"Nat Biomed Eng"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-022-01735-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-022-01735-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-022-01735-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:02:23Z","timestamp":1744203743000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-022-01735-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1735"],"URL":"https:\/\/doi.org\/10.1007\/s13198-022-01735-w","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"type":"print","value":"0975-6809"},{"type":"electronic","value":"0976-4348"}],"subject":[],"published":{"date-parts":[[2022,8,19]]},"assertion":[{"value":"25 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors assure don\u2019t have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors assure no animals and humans involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"Not Applicable to this research.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}