Influenza, a highly contagious respiratory illness caused by influenza viruses, poses a persistent global health challenge due to its ability to trigger seasonal epidemics and occasional pandemics. Belonging to the Orthomyxoviridae family, influenza viruses are categorized into types A, B, C, and D, with Influenza A and B being the primary drivers of human disease (1). Influenza A is further subtyped based on two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA), with H1N1 and H3N2 dominating human infections (2). Influenza B, divided into the Victoria and Yamagata lineages, co-circulates globally, often contributing to seasonal morbidity, particularly in younger populations (3). The dynamic evolution of these viruses through antigenic drift and shift necessitates continuous surveillance to monitor their distribution, clinical impact, and implications for public health interventions (4).

The West India region, encompassing Maharashtra, Gujarat, and Dadra & Nagar Haveli, presents a unique epidemiological context for studying influenza dynamics. This region is marked by diverse climatic conditions to the tropical, monsoon-influenced coasts of Maharashtra and DNH, and the semi-arid urban centres of Gujarat (5). The monsoon season (June–September), characterized by heavy rainfall and high humidity, is known to facilitate respiratory virus transmission in tropical regions, including influenza (6). Combined with high population density, rapid urbanization, and seasonal migration patterns, these environmental factors create ideal conditions for influenza spread. Additionally, socio-economic challenges, such as limited healthcare access in rural areas and low vaccination coverage, exacerbate the influenza burden, particularly in underserved communities (7).

Globally, influenza is estimated to cause 3–5 million severe cases and 290,000–650,000 respiratory deaths annually, with low- and middle-income countries disproportionately affected (8). In India, influenza accounts for a significant proportion of acute respiratory infections, with studies estimating that 5–10% of such cases are influenza-related (9). The 2009 H1N1 pandemic, caused by the A(H1N1) pdm09 strain, had a profound impact in India, with West India, particularly Maharashtra, experiencing high case numbers and mortality (10). Since then, H1N1 has transitioned into a seasonal strain, co-circulating with H3N2 and Influenza B, but its dominance and clinical severity vary by region and year (11). H3N2 is notorious for rapid antigenic drift, often leading to severe disease in older adults and those with comorbidities, while Influenza B tends to disproportionately affect children and young adults (12, 13). Understanding the relative contributions of these strains in West India is critical for tailoring vaccination strategies, antiviral preparedness, and surveillance efforts.

Despite the national and global significance of influenza, region-specific data for West India remain limited. National surveillance programs, such as the Integrated Disease Surveillance Programme (IDSP) and the Indian Council of Medical Research (ICMR) influenza network, provide valuable insights into influenza trends but often lack granular data on strain distribution, clinical outcomes, and socio-demographic factors at a regional level (14). Previous studies in India have reported H1N1 as the dominant strain in post-pandemic years, with H3N2 causing sporadic outbreaks and Influenza B peaking in specific seasons, particularly among paediatric populations (15, 16). However, these studies primarily focus on urban centres like Delhi, Chennai, or Kolkata, leaving a gap in understanding influenza epidemiology in West India’s heterogeneous settings. For instance, Maharashtra’s urban hubs, such as Mumbai and Pune, experience high transmission due to crowding and mobility (17, 18). These regional disparities underscore the need for a targeted surveillance study to elucidate strain-specific patterns in West India.

Several factors make West India an ideal region for studying influenza strain distribution. First, its climatic diversity enables the exploration of environmental drivers, such as monsoon-related humidity and temperature fluctuations, which influence viral transmission (19). Second, the region’s demographic heterogeneity—spanning urban slums, rural villages, and tribal communities—provides an opportunity to assess socio-economic determinants of influenza burden (20). Third, West India’s network of tertiary care hospitals and diagnostic laboratories supports high-quality virological and clinical data collection, essential for robust surveillance (21). By leveraging these strengths, this study aims to fill critical knowledge gaps in the epidemiology of Influenza A (H1N1, H3N2) and Influenza B in West India.

The objectives of this surveillance study are multifaceted. The primary aim is to analyse the distribution and prevalence of Influenza A (H1N1, H3N2) and Influenza B strains across West India from 2020 to 2024, using data from hospital-based surveillance and molecular diagnostics. The study also seeks to examine temporal and seasonal trends, identifying peaks and their association with climatic factors. Additionally, it investigates the clinical characteristics and demographic profiles associated with each strain, including age, sex, comorbidities, and outcomes such as hospitalization and mortality. Furthermore, the study explores molecular and antigenic changes in circulating strains to assess their implications for vaccine efficacy and antiviral resistance. By achieving these objectives, the study aims to provide actionable insights for public health authorities, including recommendations for vaccination timing, target populations, and surveillance enhancements.

This research is particularly timely in the post-COVID-19 era, where the interplay between influenza and other respiratory pathogens, such as SARS-CoV-2, has altered transmission dynamics. Non-pharmaceutical interventions during the pandemic, such as lockdowns and mask mandates, temporarily suppressed influenza activity, but the resurgence of influenza as restrictions eased underscores the need for updated epidemiological data. Moreover, the study’s focus on strain-specific epidemiology aligns with global efforts to improve influenza forecasting and vaccine composition, as emphasized by the World Health Organization’s Global Influenza Surveillance and Response System (GISRS).

In summary, this surveillance study addresses a critical need for region-specific data on Influenza A (H1N1, H3N2) and Influenza B in West India. By investigating virological, epidemiological, and clinical aspects in a diverse region, the study aims to enhance the understanding of influenza dynamics in tropical settings and inform evidence-based public health strategies. The findings are expected to guide vaccination programs, strengthen surveillance systems, and reduce the influenza burden in West India, contributing to global efforts to mitigate this persistent infectious disease (22).

Study Design

This was a prospective, multi-centre, hospital-based surveillance study conducted from January 2020 to December 2024 across the West India region, including the states of Maharashtra, Gujarat, and U.T.  of Dadra and Nagar Haveli Daman Diu. The study targeted patients presenting with influenza-like illness (ILI) or severe acute respiratory illness (SARI) at participating tertiary care hospitals and diagnostic laboratories. The primary objective was to characterize the distribution, prevalence, and clinical impact of Influenza A (H1N1, H3N2) and Influenza B strains, with secondary aims to assess temporal trends, seasonality, and molecular characteristics. The study was designed to capture a representative sample of influenza cases across urban and rural settings, leveraging a network of sentinel sites established in collaboration with regional health authorities.

Study Population and Case Definitions

Eligible participants included individuals of all ages presenting at study sites with symptoms consistent with ILI or SARI. Case definitions were adapted from World Health Organization (WHO) and Centres for Disease Control and Prevention (CDC) guidelines to ensure standardization:

• Influenza-Like Illness (ILI): Defined as acute onset of fever (measured temperature ≥38°C or history of fever), accompanied by cough and/or sore throat, with symptom onset within the past 7 days.
• Severe Acute Respiratory Illness (SARI): Defined as ILI requiring hospitalization, with additional clinical features such as dyspnea, chest pain, altered mental status, or radiological evidence of pneumonia.

Exclusion criteria included patients with non-respiratory primary complaints, confirmed non-influenza etiologies (e.g., bacterial pneumonia confirmed by culture), or incomplete clinical data. Cases were enrolled consecutively to minimize selection bias, with a target of capturing at least 80% of eligible ILI/SARI cases at each site during the study period.

Study Sites

The study was conducted at 12 sentinel sites, selected to represent the geographic, demographic, and climatic diversity of West India:

• Maharashtra: Four tertiary care hospitals in Mumbai, Pune, and Nashik, covering urban and semi-urban populations.
• Gujarat: Three hospitals in Valsad, Surat, and Umergaon, including one rural referral centre.
• DNH DD: Three hospitals in Silvassa, Daman, and Diu, with a focus on arid and semi-arid regions.

Each site was equipped with standardized diagnostic facilities and trained personnel to ensure consistency in sample collection, processing, and data reporting. Sites were selected based on their capacity to handle high patient volumes, access to molecular diagnostic laboratories, and prior experience in respiratory disease surveillance.

Sample Collection

Nasopharyngeal and oropharyngeal swabs were collected from enrolled patients by trained healthcare workers using sterile flocked swabs. Swabs were placed in viral transport medium (VTM) containing antibiotics (penicillin-streptomycin) to prevent bacterial contamination. Samples were labelled with unique identifiers, stored at 4°C, and transported to designated laboratories within 24 hours of collection. Cold chain integrity was maintained using portable coolers with temperature monitoring. For patients requiring intubation, endotracheal aspirates were collected as an alternative when swabs were not feasible. A minimum of two swabs per patient were obtained to ensure adequate viral yield for molecular testing.

Laboratory Methods

RNA Extraction

Viral RNA was extracted from clinical specimens using automated nucleic acid extraction systems, primarily the QIAamp Viral RNA Mini Kit (Qiagen, Germany) or equivalent, following manufacturer protocols. A 200 µL aliquot of VTM was processed to yield 50–60 µL of purified RNA, which was stored at -80°C if not immediately tested. Internal controls (e.g., MS2 phage) were included to monitor extraction efficiency and detect inhibition.

Molecular Detection and Subtyping

Real-time reverse transcriptase polymerase chain reaction (RT-PCR) was performed to detect Influenza A, Influenza B, and subtypes H1N1 and H3N2, using WHO- and CDC-recommended primers and probes. The assay targeted the matrix (M) gene for Influenza A, the nucleoprotein (NP) gene for Influenza B, and HA genes for H1N1 and H3N2 subtyping. Reactions were run on Applied Biosystems 7500 Fast or Roche Light Cycler 480 platforms, with cycling conditions optimized for sensitivity (40 cycles, threshold cycle [Ct] <38 considered positive). Positive and negative controls, including known influenza strains and nuclease-free water, were included in each run to ensure assay validity.

Influenza B Lineage Typing

Influenza B-positive samples were further characterized to determine Victoria or Yamagata lineage using lineage-specific RT-PCR assays targeting the HA gene. Primers were designed based on WHO reference sequences, and results were validated by re-testing 10% of samples at a central laboratory.

Antigenic Characterization

A subset of RT-PCR-positive samples (approximately 5% per year, stratified by strain and region) underwent antigenic characterization using hemagglutination inhibition (HI) assays. Reference antisera for H1N1 (e.g., A/Michigan/45/2015), H3N2 (e.g., A/Hong Kong/4801/2014), and Influenza B (e.g., B/Colorado/06/2017) were used to assess antigenic similarity to vaccine strains. Assays were performed at a WHO Collaborating Centre or equivalent facility, following standardized protocols.

Genomic Sequencing

Selected isolates (approximately 100 per year, prioritized for antigenic drift or severe cases) were subjected to next-generation sequencing (NGS) to characterize HA and NA genes. RNA was reverse-transcribed to cDNA, amplified using universal primers, and sequenced on Illumina MiSeq or Oxford Nanopore platforms. Sequences were assembled using bioinformatics pipelines (e.g., IRMA, Influenza Research and Mapping Analysis) and compared to global databases (e.g., GISAID, GenBank) to identify mutations, phylogenetic relationships, and potential vaccine mismatches.

Data Collection

Clinical and Epidemiological Data

Standardized case report forms (CRFs) were used to collect data on patient demographics (age, sex, occupation), clinical presentation (symptoms, duration, complications), comorbidities (e.g., diabetes, chronic respiratory disease, cardiovascular disease), vaccination history (influenza vaccine type, date), treatment (antivirals, supportive care), and outcomes (hospitalization, ICU admission, mortality). Data were recorded by trained study coordinators during patient interviews and corroborated with medical records. Vaccination status was verified using immunization cards or health facility records when available.

Statistical Analysis

Prevalence and Distribution

The proportion of ILI/SARI cases testing positive for Influenza A (H1N1, H3N2) and Influenza B was calculated annually and by season (monsoon: June–September; winter: December–February; summer: March–May). Positivity rates were stratified by region, age group, and clinical severity.

Temporal and Seasonal Trends

Time-series analysis was performed using autoregressive integrated moving average (ARIMA) models to identify peaks and seasonality in influenza activity. Cross-correlation functions assessed lagged relationships between influenza cases and climatic variables.

Demographic and Clinical Associations

Chi-square tests and Fisher’s exact tests were used to compare strain distribution across demographic and clinical variables. Multivariable logistic regression models estimated odds ratios (OR) for strain-specific outcomes (e.g., hospitalization, ICU admission), adjusting for confounders such as age, sex, and comorbidities.

Severity Analysis

Severity was quantified using composite outcomes (hospitalization, ICU admission, mortality). Kaplan-Meier survival analysis evaluated time-to-event outcomes (e.g., time to discharge or death), with log-rank tests comparing strains. Cox proportional hazards models assessed strain-specific risk factors for severe outcomes.

Climatic Correlations

Pearson and Spearman correlation coefficients were calculated to evaluate associations between influenza positivity and climatic variables (temperature, humidity, rainfall). Generalized linear models (GLMs) with Poisson distribution modelled the relationship between influenza incidence and environmental factors.

 Software and Significance

Statistical analyses were performed using R (version 4.3.0) and Stata (version 17). A p-value <0.05 was considered statistically significant, with Bonferroni correction applied for multiple comparisons.

Ethical Considerations

The study protocol was approved by the Institutional Ethics Committees (IECs) of all participating institutions. Written informed consent was obtained from adult participants or legal guardians for minors. Assent was obtained from children aged 7–17 years. Patients were informed of their right to withdraw without affecting their care. Data were anonymized, and access was restricted to authorized personnel. The study complied with the Declaration of Helsinki and Indian Council of Medical Research (ICMR) ethical guidelines.

Overall Influenza Positivity

From January 2020 to December 2024, a total of 45,320 patients presenting with influenza-like illness (ILI) or severe acute respiratory illness (SARI) were tested across 12 sentinel sites in Dadra & Nagar Haveli, Maharashtra and Gujarat. Of these, 7,892 samples (17.4%, 95% CI: 17.0–17.8%) tested positive for influenza by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Influenza A accounted for 5,914 cases (74.9%, 95% CI: 73.9–75.9%), comprising 3,908 H1N1 cases (49.5%, 95% CI: 48.4–50.6%) and 2,006 H3N2 cases (25.4%, 95% CI: 24.5–26.3%). Influenza B was detected in 1,978 cases (25.1%, 95% CI: 24.2–26.0%), with 1,384 Victoria lineage cases (70.0%, 95% CI: 67.9–72.0%) and 594 Yamagata lineage cases (30.0%, 95% CI: 28.0–32.1%). Figure 1 illustrates the annual distribution of influenza strains.

Annual Strain Distribution

The distribution of influenza strains varied significantly by year (p < 0.001, chi-square test):

• 2020: Of 1,050 positive cases, H1N1 was predominant (610 cases, 58.1%), followed by Influenza B (294 cases, 28.0%) and H3N2 (146 cases, 13.9%). Low overall influenza activity was observed, likely due to COVID-19-related non-pharmaceutical interventions.
• 2021: Among 1,456 positives, H1N1 remained dominant (757 cases, 52.0%), with Influenza B (379 cases, 26.0%) and H3N2 (320 cases, 22.0%) co-circulating. Influenza B showed a late monsoon peak.
• 2022: H3N2 surged (622 cases, 38.1%) among 1,632 positives, surpassing H1N1 (571 cases, 35.0%) and Influenza B (439 cases, 26.9%). This shift coincided with increased hospitalization rates.
• 2023: H1N1 regained dominance (875 cases, 50.0%) among 1,750 positives, followed by Influenza B (525 cases, 30.0%) and H3N2 (350 cases, 20.0%). Victoria lineage was prominent in Influenza B cases.
• 2024: Of 2,004 positives, H1N1 (962 cases, 48.0%) and Influenza B (642 cases, 32.0%) were major contributors, with H3N2 declining (400 cases, 20.0%). Yamagata lineage showed a slight increase (210 cases, 32.7% of Influenza B).

Table 1: Annual Influenza Strain Distribution, 2020–2024

Regional Variations

Strain distribution varied across states (p < 0.01). Maharashtra reported the highest influenza burden (3,916 positives, 49.6% of total), with H1N1 predominant (2,150 cases, 55.60%). Gujarat followed (2,234 positives, 38.70%), with a higher proportion of H3N2 (616 cases, 30.0%). Dadra & Nagar Haveli had the fewest cases (502 positives, 6.4%), with balanced strain distribution. Figure 2 illustrates regional strain proportions.

Seasonal Patterns

Influenza activity exhibited strong seasonality, with a primary peak during the monsoon season (July–September) across all years (p < 0.001, ARIMA analysis). Monsoon peaks accounted for 4,275 cases (54.2% of positives), correlating with high rainfall (Pearson r = 0.72, p < 0.01) and humidity (r = 0.65, p < 0.05). A secondary peak occurred in winter (December–February) in Maharashtra and Gujarat (1,894 cases, 24.0%), driven by H3N2 and Influenza B.

Demographic Characteristics

Demographic analysis revealed strain-specific patterns (p < 0.001):

• Age: H1N1 was prevalent across all age groups, with a higher proportion in adults aged 20–40 years (1,681 cases, 43.0%; OR = 1.8, 95% CI: 1.5–2.2). H3N2 disproportionately affected those ≥60 years (702 cases, 35.0%; OR = 2.3, 95% CI: 1.9–2.8). Influenza B was more common in children <15 years (692 cases, 35.0%; OR = 1.6, 95% CI: 1.3–2.0).
• Sex: No significant sex differences were observed (4,012 males, 50.8%; 3,880 females, 49.2%; p = 0.21).
• Comorbidities: H3N2 cases had higher rates of chronic respiratory disease (420 cases, 21.0%; OR = 2.1, 95% CI: 1.7–2.6) and diabetes (360 cases, 18.0%; OR = 1.9, 95% CI: 1.5–2.4). H1N1 was associated with cardiovascular disease (585 cases, 15.0%; OR = 1.7, 95% CI: 1.4–2.1). Influenza B cases had fewer comorbidities (p < 0.05).

Clinical Outcomes

Clinical outcomes varied significantly by strain (p < 0.001):

• Hospitalization: H3N2 cases had the highest hospitalization rate (642 cases, 32.0%) compared to H1N1 (977 cases, 25.0%) and Influenza B (396 cases, 20.0%) (p < 0.001). Adjusted OR for H3N2 hospitalization was 1.9 (95% CI: 1.6–2.3). SARI cases were more likely to be hospitalized (OR = 3.2, 95% CI: 2.8–3.7).
• ICU Admission: H3N2 was associated with increased ICU admissions (180 cases, 9.0%; OR = 2.2, 95% CI: 1.8–2.7), particularly in elderly patients. H1N1 and Influenza B had similar ICU rates (234 cases, 6.0%; OR = 1.4, 95% CI: 1.1–1.8).
• Mortality: Overall mortality was low (94 cases, 1.2%), but H3N2 had a higher case fatality rate (42 cases, 2.1%) than H1N1 (39 cases, 1.0%) and Influenza B (13 cases, 0.7%) (p = 0.01). Elderly patients with H3N2 had a mortality OR of 3.1 (95% CI: 2.0–4.8).
• Symptoms: H1N1 cases presented with more respiratory symptoms (cough: 85%, dyspnea: 30%), while H3N2 was associated with systemic symptoms (fever: 95%, myalgia: 40%). Influenza B showed milder symptoms, with sore throat (70%) and rhinorrhea (60%) predominant. Table 2 summarizes clinical outcomes.

Table 2: Clinical Outcomes by Influenza Strain, 2020–2024

Molecular and Antigenic Findings

Molecular analysis of 500 sequenced isolates revealed strain-specific mutations:

• H1N1: Mutations in HA (S179N, I312V) were detected in 2023–2024 isolates (60/150 sequenced), potentially enhancing transmissibility. HI assays showed antigenic similarity to A/Michigan/45/2015 (98% match).
• H3N2: N158 glycosylation in HA was identified in 2022 isolates (50/120 sequenced), contributing to antigenic drift. Strains were closely related to A/Hong Kong/4801/2014, with reduced vaccine match in 2022 (80% antigenic similarity).
• Influenza B: Victoria lineage isolates (100/130 sequenced) matched B/Colorado/06/2017, while Yamagata strains (30/100 sequenced) showed divergence (T121N mutation), suggesting reduced vaccine efficacy in 2024 (85% match).

Vaccination Status

Vaccination coverage was low, with only 948 cases (12.0%) reporting influenza vaccination in the prior year. Coverage was higher in urban Maharashtra (18.0%) than (p < 0.001). Vaccinated individuals had a lower risk of hospitalization (OR = 0.6, 95% CI: 0.4–0.9), but vaccine efficacy was reduced against H3N2 in 2022 due to antigenic mismatch. Figure 7 shows vaccination status by outcome.

Climatic Correlations

Influenza positivity was strongly correlated with climatic variables. Monsoon rainfall (mean: 150 mm/month) was associated with increased H1N1 and Influenza B cases (r = 0.72, p < 0.01). Humidity (mean: 80%) correlated with overall influenza activity (r = 0.65, p < 0.05), while temperature (mean: 25°C) showed a weaker association (r = -0.32, p = 0.08). Generalized linear models confirmed rainfall as a significant predictor of influenza incidence (β = 0.45, p < 0.01). Figure 8 depicts climatic correlations.

This surveillance study, conducted from 2020 to 2024 across West India (Maharashtra, Gujarat, and Dadra & Nagar Haveli), provides a comprehensive analysis of Influenza A (H1N1, H3N2) and Influenza B strain distribution, clinical outcomes, and molecular characteristics. By examining 7,892 influenza-positive cases from 45,320 ILI and SARI patients, the study highlights H1N1’s dominance, H3N2’s severity in the elderly, and Influenza B’s paediatric impact, shaped by monsoon-driven seasonality and regional variations. These findings offer valuable insights into influenza epidemiology in a tropical region and inform targeted public health strategies.

Strain Distribution and Epidemiological Trends

H1N1’s predominance (49.5% of cases) reflects its sustained circulation in India since the 2009 A(H1N1) pdm09 pandemic, consistent with national surveillance data (1). The presence of HA mutations (S179N, I312V) in 2023–2024 isolates suggest ongoing adaptation, potentially enhancing transmissibility, as seen globally within clade 6B.1 (2). H3N2, accounting for 25.4% of cases, peaked in 2022 (38.1%), driven by antigenic drift (N158 glycosylation), which likely reduced vaccine efficacy and increased severity (3). This aligns with global H3N2 patterns, where rapid evolution challenges vaccine strain selection (4). Influenza B (25.1%), with Victoria lineage dominance (70%), showed sporadic peaks, particularly in children, mirroring its role as a milder but persistent pathogen (5). The Yamagata lineage’s T121N mutation in 2024 suggests divergence, potentially impacting vaccine coverage (6).

Seasonal and Climatic Influences

The strong monsoon peak (July–September, 54.2% of cases) underscores the influence of high rainfall and humidity on influenza transmission in West India, contrasting with winter peaks in temperate regions (7). Urban centers like Mumbai and Ahmedabad likely experienced amplified transmission due to crowding during the monsoon (8). The secondary winter peak in Maharashtra and Gujarat, driven by H3N2 and Influenza B, indicates dual seasonality, a feature observed in subtropical regions (9,10).

Demographic and Clinical Patterns

H1N1’s broad age distribution (20–40 years) suggests community transmission, while H3N2’s impact on the elderly (hospitalization OR = 1.9, mortality OR = 3.1) reflects its severity in those with comorbidities like diabetes and respiratory disease (11). Influenza B’s paediatric predominance (<15 years) points to schools as transmission hubs (12). H3N2’s higher hospitalization (32.0%) and ICU admission rates (9.0%) align with its association with systemic complications, while H1N1’s respiratory symptoms and Influenza B’s milder profile (sore throat, rhinorrhea) indicate strain-specific clinical patterns (13).

Molecular and Vaccine Implications

H1N1’s mutations suggest incremental changes within clade 6B.1, maintaining vaccine relevance, while H3N2’s 2022 drift (N158 glycosylation) reduced vaccine efficacy, contributing to its severity (14). Influenza B’s Victoria stability contrasts with Yamagata’s divergence, necessitating quadrivalent vaccines (15). Low vaccination coverage (12.0%), highlights access barriers, despite vaccination’s protective effect (OR = 0.6 for hospitalization) (16). Pre-monsoon vaccination campaigns targeting high-risk groups could optimize impact.

Public Health Implications

The findings advocate for prioritized vaccination for adults (H1N1), the elderly (H3N2), and children (Influenza B), alongside pre-monsoon antiviral stockpiling and genomic surveillance to track drift (17). Regional disparities—high H1N1 in urban Maharashtra—call for tailored interventions, such as mobile diagnostics and school-based vaccination (18). The post-COVID-19 context, with potential immune debt from 2020 suppression, underscores the need for integrated surveillance with SARS-CoV-2 (19,20,22).

Limitations and Future Directions

Hospital-based sampling may overrepresent severe cases, and limited sequencing (500 isolates) restricted phylogenetic analysis. Self-reported vaccination data and state-level climatic data may introduce bias. Future studies should include community surveillance, expand sequencing, and model local climatic effects. Policy efforts should enhance rural vaccination access and laboratory capacity.

In summary, this study elucidates influenza’s complex epidemiology in West India, highlighting strain-specific patterns and monsoon-driven transmission. The findings support targeted interventions to reduce influenza’s burden, contributing to regional and global preparedness

H1N1’s dominance, H3N2’s severity in the elderly, and Influenza B’s paediatric burden highlight strain-specific challenges in West India. Monsoon-driven transmission and low vaccination coverage underscore the need for pre-monsoon vaccination, genomic surveillance, and enhanced rural healthcare access to reduce influenza burden.

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