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Emergence of SARS-CoV-2 omicron subvariant NB.1.8.1 in India: Genomic evolution, transmission patterns, and public health implications
For correspondence: Dr Shikha Sharma, Department of Operations and HIPAA Compliance, Lab Testing API Inc., Wilmington 19802-4447, United States and Centre for Distance and Online Education (CDOE), Panjab University, Chandigarh 160 014, India e-mail: shikha_3107@yahoo.com
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Received: ,
Accepted: ,
How to cite this article: Sharma S, Manikyam HK. Emergence of SARS-CoV-2 omicron subvariant NB.1.8.1 in India: Genomic evolution, transmission patterns, and public health implications. Indian J Med Res. 2026;163:104-10. DOI: 10.25259/IJMR_1558_2025.
Abstract
Background and objectives
The NB.1.8.1 Omicron subvariant has demonstrated notable epidemiological relevance in India, though without evidence of a marked increase in severity compared to prior Omicron waves. Understanding its genomic trajectory and policy implications is critical.
Methods
This was a retrospective convergent mixed-methods study integrating genomic sequencing (GISAID, INSACOG), epidemiological counts (ICMR, WHO, CDC), and policy/advisory analysis (MoHFW, WHO-SEARO). Data were analysed across January–May 2025 using prevalence tracking, hospitalisation comparisons, and thematic policy review.
Results
Genomic analyses showed NB.1.8.1 carrying lineage-defining spike mutations (A435S, V445H, T478I) linked to transmissibility and immune escape. While prevalence rose in China, India reported <5% share. Hospitalisation burdens remained lower in India than in China. India’s policy response showed increasing alignment between genomic surveillance outputs and subsequent public health advisories, with targeted booster promotion and enhanced surveillance in high-incidence States.
Interpretation and conclusions
NB.1.8.1 illustrates the dynamic evolutionary trajectory of SARS-CoV-2. India’s adaptive genomic surveillance and flexible public health frameworks contributed to mitigating clinical severity, though surveillance gaps and rural under-reporting remain concerns. Sustained genomic tracking, booster equity, and real-time advisory mechanisms are needed to strengthen preparedness.
Keywords
COVID-19
Genomic surveillance
India
NB.1.8.1
Pandemic preparedness
Public health policy
The COVID-19 pandemic, now in its fifth year, continues to evolve through successive waves of SARS-CoV-2 variants. Early lineages such as Alpha and Delta triggered severe global surges, whereas Omicron, first identified in late 2021, became the dominant strain due to its high transmissibility and immune-evasion capacity.1,2 Omicron further diversified into sublineages such as BA.2, XBB, and JN.1, each exhibiting distinct genomic and clinical profiles that shaped regional and global transmission patterns.3,4
By early 2025, a descendent Omicron subvariant designated NB.1.8.1 emerged within the JN.1 clade and was classified by the World Health Organization (WHO) as a variant under monitoring (VUM) because of its increasing detections and distinctive mutation profile.5 This subvariant carries three lineage-defining spike substitutions—A435S, V445H, and T478I—linked to altered ACE2 receptor binding, partial antibody-neutralisation escape, and enhanced viral fitness.6,7 Reports from East and South Asia indicated a steady rise in NB.1.8.1 prevalence during March–May 2025, suggesting a transmission advantage over other Omicron descendants.8,9
In India, the Indian SARS-CoV-2 Genomics Consortium (INSACOG) and the Indian Council of Medical Research (ICMR) first detected NB.1.8.1 in Tamil Nadu and Maharashtra in April 2025.10,11 Subsequent sequencing revealed an increasing frequency of the variant across southern and western regions, coinciding with uneven booster uptake and disparities in genomic coverage.12,13 In contrast, surveillance data from China and Hong Kong showed higher hospitalisation rates and distinct vaccination trends, underscoring cross-national differences in health-system capacity and booster strategies.14,15
Although preliminary evidence suggested that NB.1.8.1 infections were largely mild, India’s decentralised surveillance system, uneven genomic sampling, and inconsistent booster response necessitated a more integrated assessment linking genomic, epidemiological, and policy perspectives.16,17 Understanding how emerging variants interact with vaccination coverage, genomic surveillance, and public health readiness became crucial for national preparedness.18,19
Based on these identified gaps, the present study was designed to provide an integrated genomic, epidemiological, and policy analysis of NB.1.8.1 emergence in India. Specifically, it aimed to examine the temporal trend and relative growth of NB.1.8.1 compared with LP.8.1 and other Omicron subvariants using GISAID data between April and May 2025; to compare hospitalisation burdens between India and China using ICMR and Chinese Centre for Disease Control and Prevention (Chinese CDC) surveillance bulletins; and to characterise the spike mutations defining the NB.1.8.1 lineage while evaluating their potential role in transmission and immune evasion. The study also contextualised these findings within India’s vaccination coverage, genomic-surveillance capacity, and overall pandemic-readiness framework.9,11
In alignment with these objectives, three hypotheses were formulated. The first hypothesis (H1) proposed that the prevalence of NB.1.8.1 in India demonstrates a measurable association with weekly genomic-surveillance trends, The second hypothesis (H2) posited that hospitalisation outcomes in India differ significantly from those in China. The third hypothesis (H3) suggested that vaccination and booster coverage are significantly associated with hospitalisation outcomes, Together, these hypotheses established a coherent framework to explore NB.1.8.1’s genomic evolution, transmission potential, and clinical implications, while linking results to policy and health-system preparedness. The study’s integrative design sought to bridge molecular surveillance and public-health governance to strengthen India’s pandemic-readiness infrastructure.
Methods
This retrospective, convergent mixed-methods study was undertaken by the Centre for Distance and Online Education (CDOE), Panjab University, Chandigarh, India. All datasets used were aggregated, anonymised, and publicly accessible; therefore, formal ethics clearance was not required under the ICMR National Ethical Guidelines (2021). Data access complied with open-data policies of GISAID, ICMR, Ministry of Health and Family Welfare (MoHFW), WHO, and China CDC. The appendices include accession identifiers, denominators, and confidence-interval templates to ensure full transparency and reproducibility of analyses.
The study integrated quantitative genomic and epidemiological data with qualitative policy information to investigate the emergence and early transmission dynamics of the SARS-CoV-2 Omicron subvariant NB.1.8.1 in India. Quantitative parameters included variant prevalence, hospitalisation counts, and vaccination coverage, while qualitative inputs comprised government advisories, surveillance bulletins, and validated media sources. The study period extended from mid-January to May 2025, corresponding to the detection window of NB.1.8.1 as reported by the Indian SARS-CoV-2 Genomics Consortium (INSACOG) and the Indian Council of Medical Research (ICMR).9,10 Quantitative and qualitative strands were analysed in parallel and merged during interpretation, ensuring methodological triangulation and contextual depth. Such a convergent design is appropriate for pandemic-era investigations where rapidly evolving genomic evidence intersects with health-system responses.20,21
Rationale for scale and timeframe
The analysis employed a national-level comparative scale focusing on India and China to highlight systemic contrasts in genomic-surveillance capacity, hospital-admission trends, and booster-dose rollout. Weekly intervals were chosen because both ICMR and the China CDC release epidemiological bulletins on a weekly basis, facilitating synchronised trend comparisons.8,11
The observation window (January–May 2025) captured the complete emergence phase of NB.1.8.1: January marked its first global and Indian genomic registrations in the GISAID EpiCoV and INSACOG repositories, whereas May represented the plateau stage in genomic prevalence and hospitalisation patterns. Restricting the analysis to this five-month interval avoided confounding by later recombinant subvariants and permitted a focused statistical evaluation aligned with contemporaneous national bulletins.13,15
Data sources
Data were compiled from validated open repositories. Genomic sequences were obtained from GISAID EpiCoV and INSACOG submissions.9,10 Epidemiological and hospitalisation data were drawn from ICMR weekly COVID-19 bulletins and the Chinese CDC situation reports.8,11 Vaccination and booster-coverage statistics were taken from the Ministry of Health and Family Welfare (MoHFW) dashboards, with supplementary context from WHO technical updates and Our World in Data aggregates.1,18 Background immunity indicators were derived from ICMR serosurveys and MoHFW booster datasets, while peer-reviewed publications provided mechanistic insights into spike-protein mutations.4,16
Only sequences meeting predefined quality thresholds—complete genome length, coverage > 90 per cent, verified metadata (collection date and country of origin), and unique accession ID—were retained. Sequences with incomplete metadata, low coverage, or duplication were excluded. After filtration, 845 Indian and 410 Chinese NB.1.8.1 genomes were included (Supplementary Table I). Comparator lineages (JN.1, XBB, XEC) were analysed concurrently to delineate lineage-specific patterns. Although NB.1.8.1 constituted less than 1% of Omicron sequences in India during the study period, inclusion was justified under early-warning surveillance principles since low-frequency variants such as XBB had previously expanded rapidly.5,17
Data collection and processing
A structured workflow was followed to maintain consistency and reproducibility. Genomic sequences retrieved from GISAID were cross-verified with INSACOG records; comparator lineages were aligned to the Wuhan-Hu-1 reference genome for mutational mapping. Weekly hospitalisation and confirmed-case denominators were extracted from ICMR and China CDC bulletins, while vaccination coverage and booster uptake were synchronised from MoHFWdashboards.8,11 Records missing denominators or booster data were excluded to preserve analytical integrity.
Descriptive summaries of weekly genomic prevalence and hospitalisation trends are presented in Tables I and II. Complete denominators, sample sizes, and metadata are tabulated in Appendices A–E to facilitate replication. Mutation profiles of NB.1.8.1 relative to other Omicron sublineages (JN.1, XBB, XEC) were characterised using amino-acid substitution matrices (Table III; Supplementary Table II). Structural and functional implications of lineage-defining mutations A435S, V445H, and T478I were interpreted with reference to published molecular-virology analyses.6,10
| Week | Total sequences in GISAID (N) | NB.1.8.1 n (%) | LP.8.1 n (%) | Other Omicronn (%) |
|---|---|---|---|---|
| 14 | xxxx | xxx (2.5) | xxx (5.8) | xxx (91.7) |
| 15 | xxxx | xxx (4.2) | xxx (5.0) | xxx (90.8) |
| 16 | xxxx | xxx (6.7) | xxx (4.2) | xxx (89.1) |
| 17 | xxxx | xxx (8.3) | xxx (3.9) | xxx (87.8) |
| 18 | xxxx | xxx (10.7) | xxx (3.2) | xxx (86.1) |
N denotes the total number of SARS-CoV-2 genomic sequences available in the GISAID database globally for each epidemiological week and used as the denominator for prevalence calculations. xxxx indicates the corresponding weekly total sequence counts obtained from the GISAID database for the specified weeks. n (%) represents the number and percentage of sequences assigned to each lineage. Chi-square test of trend: χ2(4, N=5) = 7.48, P = 0.113; Cramer’s V = 0.35 (moderate effect size).
Source: GISAID global variant dashboard7.
| Week | India (per 1,000 cases) | China (per 1,000 cases) |
|---|---|---|
| 14 | xx (95% CI x–x) | xx (95% CI x–x) |
| 15 | xx (95% CI x–x) | xx (95% CI x–x) |
| 16 | xx (95% CI x–x) | xx (95% CI x–x) |
| 17 | xx (95% CI x–x) | xx (95% CI x–x) |
| 18 | xx (95% CI x–x) | xx (95% CI x–x) |
| Mean (Weeks 14–18) | 2.12 [95% CI (1.96, 2.28)] | 3.84 [95% CI (3.50, 4.18)] |
xx denotes the weekly COVID-19 hospitalisation rate per 1,000 confirmed cases derived from national surveillance datasets for the respective epidemiological week. 95% CI indicates the 95 per cent confidence interval calculated from reported weekly case and hospitalisation totals. Independent-samples t-test: t(8) = -12.82, P < 0.001; Cohen’s d = 8.09 (very large effect size).
| Mutation | Functional role | NB.1.8.1 | JN.1 | XBB | XEC |
|---|---|---|---|---|---|
| A435S | Alters spike conformation; reduces antibody recognition | ✔ | – | – | – |
| V445H | Enhances ACE2 binding | ✔ | – | – | – |
| T478I | Partial immune evasion; also observed in Delta | ✔ | – | – | – |
| K417N | Widespread across Omicron subvariants | – | ✔ | ✔ | ✔ |
| S486P | Widespread across Omicron subvariants | – | ✔ | ✔ | ✔ |
Source: Compiled by the authors from INSACOG national submissions and the GISAID genomic database
Analytical framework
All statistical analyses were performed using IBM SPSS version 29.0. Weekly genomic-prevalence trajectories (H1) were evaluated through Chi-square tests for trend, with Cramer’s V providing effect-size estimates (0.1 = small, 0.3 = medium, ≥ 0.5 = large). Hospitalisation rates (per 1,000 confirmed cases) for India and China (H2) were compared via independent-samples t-tests; differences were interpreted using Cohen’s d and 95 per cent confidence intervals. The relationship between vaccination coverage and hospitalisation burden (H3) was assessed through Spearman’s ρ with corresponding 95% intervals.
Mutation mapping focused on spike-region alterations A435S, V445H, and T478I, given their established influence on viral fitness and immune escape.6,7 Variant sequences were aligned against the reference Wuhan-Hu-1 genome, and amino-acid substitutions were annotated by functional category. Visualisation and trend plots were generated in Microsoft Excel and Tableau. Statistical outputs are summarised in Table IV.
| Hypothesis | Objective | Statistical test | Outcome | Conclusion |
|---|---|---|---|---|
| H1: NB.1.8.1 prevalence has significantly increased over time | Growth trajectory vs LP.8.1 | Chi-square test of trend; Cramer’s V | P =0.113; V = 0.35 (moderate) | Not accepted – upward movement observed, not statistically significant |
| H2: Hospitalisation rates differ between India and China | Clinical severity comparison | Independent-samples t-test; Cohen’s d | P < 0.001; d = 8.09 (very large) | Accepted – China higher hospitalisation burden |
| H3: NB.1.8.1 mutations confer enhanced transmissibility/immune evasion | Mutation profile | Comparative genomic mapping | A435S, V445H, T478I enriched in NB.1.8.1 | Accepted – biologically plausible enhanced fitness |
Source: Prepared by the authors using data derived from INSACOG, ICMR, and the GISAID database
Results
Genomic prevalence and growth dynamics
GISAID sequencing data from weeks 14–18 (April–May 2025) showed a steady global increase in NB.1.8.1 prevalence from 2.5 to 10.7 per cent, accompanied by a decline in LP.8.1 from 5.8 to 3.2 per cent. Weekly prevalence data are summarised in Table I, with denominators available in (Supplementary Table I).
A chi-square test for trend yielded χ2=7.48 (P=0.113), and Cramer’s V=0.35, indicating a moderate but non-significant upward association. While Hypothesis H1 was not accepted, the consistent rise across five consecutive weeks suggests meaningful epidemiological movement. These findings parallel observed patterns from other early Omicron recombinants, where moderate but steady increases preceded dominance.5,6
Regional hospitalisation comparison
India vs. China: Hospital data compiled from ICMR and Chinese CDC reports7,14 demonstrated divergent trends. India maintained largely stable hospitalisation rates despite rising NB.1.8.1 detections in Tamil Nadu, Maharashtra, and Delhi, while China recorded a marked increase. Weekly hospitalisation rates per 1,000 confirmed cases are presented in Table II, with denominators in Supplementary Material I.
Between weeks 14–18, mean hospitalisation was 2.12 (95% CI 1.96–2.28) in India and 3.84 (95% CI 3.50–4.18) in China. Independent-samples t-test results confirmed a significant difference (t(8) = –12.82, P <0.001) with a large effect size (Cohen’s d = 8.09).
ICMR serosurveys and MoHFW booster dashboards corroborated India’s lower clinical burden, plausibly reflecting heterogeneous booster uptake and strong background immunity.7,14 Hypothesis H2 was therefore accepted, confirming higher hospitalisation rates in China during the same period.
Mutation mapping and functional implications
Genomic characterisation identified three lineage-defining spike substitutions—A435S (altered spike conformation, reduced antibody recognition),17 V445H (enhanced ACE2 binding),5 and T478I (partial immune evasion, previously reported in Delta).4 These collectively confer potential fitness advantages. Spike-mutation distributions are shown in Table III, with detailed comparisons across JN.1, XBB, and XEC (Supplementary Table II).
A total of 845 Indian and 410 Chinese NB.1.8.1 sequences were analysed. The distinct clustering of A435S, V445H, and T478I validates Hypothesis H3, indicating enhanced transmissibility and immune-evasion potential relative to other Omicron subvariants.
India-specific genomic surveillance insights
NB.1.8.1 accounted for less than 5% of sequenced cases in India, primarily in Tamil Nadu, Maharashtra, and Delhi.6,14 Sequencing density remained inconsistent, with under-representation in northern states and delays in data release. India’s mild hospitalisation trends may stem from pre-existing immunity, urban booster campaigns, and pandemic fatigue influencing reporting7,19 (Supplementary material II). Despite limited spread, the variant’s detection underscores the importance of maintaining State-level genomic vigilance.
Effectiveness of India’s public-health response
ICMR and MoHFW advisories emphasised continued surveillance and booster uptake, though regional participation was uneven. No surge in ICU admissions or mortality was recorded. Supplementary Material III presents a representative advisory template illustrating proactive communication during variant monitoring. India’s decentralised system demonstrated resilience, yet persistent gaps in sequencing coordination, booster outreach, and real-time data integration indicate scope for improvement in pandemic-readiness mechanisms.
Discussion
This study examines the emergence of the SARS-CoV-2 Omicron subvariant NB.1.8.1 in India between April and May 2025 using combined genomic, epidemiological, and policy data. A steady weekly rise in NB.1.8.1 prevalence was observed, whereas hospitalisation rates in India remained significantly lower than those reported in China. Comparative genomic analysis identified three lineage-defining spike substitutions—A435S, V445H, and T478I—that may influence receptor binding and immune evasion. Despite these evolutionary changes, the clinical burden in India remained relatively stable, which may reflect high booster coverage together with ongoing surveillance through INSACOG and ICMR networks.1,2
The growth pattern of NB.1.8.1 follows broader trends seen in recombinant Omicron lineages since 2023, where sublineages such as XBB and JN.1 accumulated convergent mutations associated with increased transmissibility and partial antibody escape.3,4 Its early detection coincided with increased genomic submissions from southern States, highlighting the role of regional sequencing capacity in recognising emerging transmission signals.5 By epidemiological week 18 (2025), NB.1.8.1 accounted for 10.7 per cent of sequences, a pattern similar to observations from Hong Kong and parts of East Asia.6 However, indicators of severe disease did not increase proportionately, suggesting that vaccination-driven hybrid immunity continues to limit severe outcomes.
A comparison with China showed a lower hospitalisation burden in India. Differences in population immunity, booster uptake, and post-infection seroprevalence may partly explain this divergence.7,8 High vaccination coverage, particularly booster administration under the CoWIN programme, likely contributed to moderating clinical severity,9 while prior exposure to multiple Omicron subvariants may have reinforced cross-neutralising immunity, as reported for BA.2.75 and XBB lineages.10 In contrast, delayed booster deployment and narrower infection history may have contributed to comparatively higher hospitalisation rates in China.11
The mutations identified in NB.1.8.1 occur in regions of functional relevance within the spike protein. Substitutions A435S and V445H are located near the receptor-binding domain and have been associated with increased ACE2 affinity,12 whereas T478I, previously observed in the Delta variant, has been linked to immune evasion through structural changes in the spike protein.13 These alterations may explain the moderate growth advantage of NB.1.8.1 over co-circulating sublineages such as LP.8.1. At the same time, the absence of a measurable increase in hospitalisation suggests that transmissibility may have risen without a comparable rise in virulence. Continued genomic monitoring and phenotypic studies remain necessary to clarify these relationships.
The relationship between vaccination coverage and reduced hospitalisation highlights the continued importance of booster programmes. Studies from India and other low- and middle-income countries have reported that booster doses substantially reduce severe COVID-19 during Omicron waves.14 In addition, the convergence of sequencing, epidemiological, and hospitalisation datasets from INSACOG, ICMR, and MoHFW illustrates the value of integrated genomic–clinical surveillance systems for guiding public-health decisions.15
These observations also carry implications for preparedness. Sequence-linked epidemiological dashboards capable of generating near-real-time alerts would strengthen early warning systems. Expanding equitable genomic coverage across states and strengthening hospital-linked surveillance networks would further improve early detection and clinical correlation. India’s experience suggests that hybrid immunity, supported by adaptive booster strategies and decentralised genomic oversight, can moderate public-health risk even during rapid viral evolution. A representative framework for integrating surveillance, vaccination, and clinical-capacity indicators into district-level preparedness assessment (Supplementary Table III).
The findings are consistent with earlier national and international evidence indicating that sustained genomic monitoring reduces uncertainty during variant transitions.16,17 Nevertheless, several structural challenges remain, including variability in sequencing density, the need for improved integration between public and private laboratories, and delays in sequence submission to global repositories that may limit real-time responsiveness.18 Addressing these gaps will be important for strengthening preparedness and maintaining transparency in public-health communication.
Certain limitations should be considered. Uneven sequencing representation across Indian States may affect the generalisability of prevalence estimates, and delays in reporting may influence temporal comparisons. Cross-country analyses may also be affected by differences in surveillance definitions and reporting frameworks.19 However, triangulation of data from multiple repositories—GISAID, ICMR, INSACOG, and Chinese CDC—improved consistency and reduced potential bias. The analysis relied on aggregated genomic and epidemiological datasets rather than individual-level clinical records, but multi-source verification and transparent methodological documentation strengthened the reliability of the findings.
In summary, NB.1.8.1 represents a further stage in the adaptive evolution of the Omicron lineage, characterised by a moderate transmissibility advantage without a corresponding increase in clinical severity in the Indian context. The findings emphasise the continuing importance of genomic surveillance, vaccination coverage, and coordinated public-health preparedness in mitigating the impact of emerging SARS-CoV-2 variants in large and diverse populations.
Acknowledgment
Authors acknowledge and thank the Indian SARS-CoV-2 Genomics Consortium (INSACOG), the Indian Council of Medical Research (ICMR), the Ministry of Health and Family Welfare, Government of India (MoHFW), the World Health Organization (WHO), and the Global Initiative on Sharing All Influenza Data (GISAID) for making data and public health resources accessible, which supported this analysis.
Author contributions
SS: Conceptualization, study design, data curation, methodology, formal analysis, interpretation of results, manuscript writing; HKM: Critical review of the manuscript, academic guidance. All authors have read and approve the final printed version of the manuscript.
Financial support and sponsorship
None.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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