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Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts

Author

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  • Amalraj Irudayasamy

    (University of Technology and Applied Sciences – Nizwa)

  • D. Ganesh

    (Jain [Deemed-to-be] University)

  • M. Natesh

    (Vidyavardhaka College of Engineering)

  • N. Rajesh

    (University of Technology and Applied Sciences-Shinas)

  • Umi Salma

    (Jazan University)

Abstract

New SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus Type-2) variant termed to be “B.1.1.529” subtype mutation, which is a primary concern, might heavily influence further transmission, virulence and even affect the functioning of test methods and efficacy medications (vaccines). It is still not clear on the timeline for the Omicron (B.1.1.529) subtype to develop protective immunity or even when normal activities will rebound in our everyday lives. Computational analysis on the available big dataset of the Omicron variants’ and their effects on the unvaccinated population indicate that the concerned variant seemed to have a stronger propensity for the vulnerable group (unvaccinated community). In consequence of the terrible COVID-19 epidemic, scientific research on vaccine development and their future enhancement throughout the world have been stepped up significantly. We assessed approved vaccines’ effect on morbidity, hospital stays, and fatalities worldwide. Through available big datasets, an Ensemble learning strategy was used to estimate the likelihood of an unvaccinated person contracting a virus. Overall incidence rates dropped from 18.56 per cent to 2.8 per cent for the vaccinated community during the observation period. People ≥ 60 years had the most remarkable percentage drop (~ 15 per cent). In addition, about 70.4 per cent, immunization through vaccines lowered the number of hospitalizations (both ICU and non-ICUs) and fatalities. Through our research observations, the facts clear that immunization through vaccination has a significant influence on decreasing COVID-19 rapid transmission, even if it provides only a modest level of protection. However, to accomplish this effect, non-pharmaceutical therapies must be maintained indefinitely.

Suggested Citation

  • Amalraj Irudayasamy & D. Ganesh & M. Natesh & N. Rajesh & Umi Salma, 2024. "Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 346-355, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01735-w
    DOI: 10.1007/s13198-022-01735-w
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    1. Wenhua Liang & Jianhua Yao & Ailan Chen & Qingquan Lv & Mark Zanin & Jun Liu & SookSan Wong & Yimin Li & Jiatao Lu & Hengrui Liang & Guoqiang Chen & Haiyan Guo & Jun Guo & Rong Zhou & Limin Ou & Niyun, 2020. "Early triage of critically ill COVID-19 patients using deep learning," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    2. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    3. Ardvin Kester S. Ong & Thanatorn Chuenyindee & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Michael N. Young & , 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”," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
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