Emerging Novel SARS-CoV-2 Subvariants and the Advantages of AI-ML in Deciphering the Mutation Trends, Genomic Surveillance and Vaccine Development.

Authors

Aurobinda Rout, Kumarjit Das, Ashish K Sarangi, Snehasish Mishra, Chandana Mohanty, Puneet K Singh, Swikrutee Rout, Ranjan K Mohapatra, Lawrence Sena Tuglo

Published in

Health science reports. Volume 9. Issue 7. Pages e72792. Epub Jul 09, 2026.

Abstract

The world witnessed COVID-19 in 2025 yet again, a resurgence driven by LF.7 and NB.1.8.1 subvariants of JN.1. With Singapore, Hong Kong and Thailand as the epicenter this time, these variants demonstrated rapid geographic spread and temporal dominance across regions including India, the United States (US), the United Kingdom (UK), and China. Genomic surveillance highlighted a sharp peak due to JN.1, followed by rising trends due to LF.7 and NB.1.8.1. Thus, a perspective was assumed aiming to get insights into the shifting epidemiological and transmission dynamics of COVID-19, and how recent advanced technologies like AI-ML could play a key role in deciphering the mutation trends of the novel virus, global genomic surveillance and vaccine research and development.
Literature on recent global COVID cases was sourced online using reliable dedicated search engines. The limited available data from sources was used to get insights. Data were further analyzed, primarily stressing on the molecular-level interplay within the virus and the host, possible technological interventions to know the infectivity and predict the transmission patterns, and the role of artificial intelligence and machine learning integration as solutions for vaccine designing to strategize community health.
India recorded over 3900 active cases in the year 2025 till early June. Kerala and Maharashtra contributed the majority of the fresh cases. Most reported cases were clinically mild, but isolated severe outcomes were witnessed, particularly among the high-risk subjects. Despite successful mass vaccination drives, recent transmission suggested a dynamic interplay of the waning immunity, adaptability of the subvariants, and altered public behavioral patterns.
Integrated AI-ML tools greatly enhanced the real-time response and resource planning through genome surveillance, predictive modeling, and outbreak predictions. In silico cutting-edge technologies are useful tools facilitating rapid assessment of the spread and strengthening decision-making in public healthcare. Designing AI-assisted strategies and continued monitoring were vital to mitigate threats of infectious diseases in the future.

PMID:
42433705
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

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