Genomic surveillance in the UAE reveals the global origins and local diversification of RSV lineages

Genomic surveillance in the UAE reveals the global origins and local diversification of RSV lineages | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic surveillance in the UAE reveals the global origins and local diversification of RSV lineages Hao Zhou, Salwa Ali, Madikay Senghore, Francis Amirtharaj, Pamela Murad, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9344370/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Respiratory syncytial virus (RSV) remains a major cause of severe respiratory disease in young children, and the rollout of vaccines and monoclonal antibodies has increased the importance of genomic surveillance. Here, we combined epidemiological analysis of 2,350 laboratory confirmed RSV infections recorded in the United Arab Emirates between 2018 and 2023 with genomic surveillance of 312 RSV positive clinical isolates collected during the 2023 to 2024 season, integrating global phylogenetic contextualization and model based variant prioritization. Severe RSV disease in the UAE was concentrated in infants and young children, who accounted for most hospital and intensive care admissions. Phylogenetic analysis showed that RSV circulation in the UAE was shaped by repeated introductions of globally circulating RSV-A and RSV-B lineages, followed by local transmission and diversification. UAE RSV-B variants also showed elevated model predicted escape burden relative to year and substitution count matched public sequences, with the strongest signals arising from a subset of circulating variants rather than the most common recurrent substitutions. Together, these findings highlight the value of surveillance in the UAE for understanding RSV circulation in a globally connected setting and show how integrated epidemiological, genomic, and evolutionary analyses can prioritize variants for continued surveillance and experimental evaluation. Biological sciences/Microbiology/Virology/Viral evolution Health sciences/Diseases/Infectious diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Respiratory Syncytial Virus (RSV) is a major global cause of acute lower respiratory tract infections (LRTIs), responsible for an estimated 33 million episodes and over 100,000 deaths in children under five annually. 1–4 After decades of challenges, the recent advent of prefusion F protein–based vaccines and long-acting monoclonal antibodies such as nirsevimab has heralded a new era in RSV prevention. 5,6 Maternal RSVpreF vaccination, nirsevimab administration in infants, and RSV vaccines for adults aged 60 years or older have each shown substantial protection, with pooled effectiveness estimates ranging from 68% to 83% against hospitalization across randomized and observational studies. 7 However, the long-term success of these interventions, which almost exclusively target the viral fusion (F) protein, is threatened by the virus's capacity for rapid antigenic drift. 8 This evolutionary pressure necessitates vigilant, genomics-informed surveillance to detect the emergence of variants that could escape neutralization and compromise these new public health tools. 9–12 At the same time, the deployment of RSV vaccines and long-acting monoclonal antibodies has outpaced the establishment of genomic baselines in many under sampled regions. This creates an important surveillance gap: without contemporaneous data on lineage turnover, introduction dynamics, and the emergence of variants of potential relevance to surveillance and prevention, it is difficult to interpret how RSV populations are changing in the prevention era. 13 Genomically informed surveillance in globally connected settings is therefore needed not only to monitor mutations in therapeutic target proteins, but also to track how international viral movement and local transmission shape the diversity of circulating RSV populations. 14,15 While surveillance is robust in some regions, the Middle East and North Africa (MENA), home to several major global civil aviation hubs, remains a critical blind spot in our understanding of global RSV circulation. 16 This data gap is particularly acute in nations that serve as major international crossroads, hindering the development of targeted public health interventions. The United Arab Emirates (UAE), a major node in global mobility and trade, provides a strategic vantage point for detecting the introduction and circulation of genetically diverse viral lineages originating from multiple geographic regions. 17,18 Understanding the molecular epidemiology of RSV in this context is therefore not just of local importance but provides a crucial window into global transmission dynamics. To address this gap, we combined multi year epidemiological surveillance with genomic analysis of RSV positive clinical isolates collected in the UAE and placed these data in a global phylogenetic context. We sought to define the age and severity distribution of RSV burden, reconstruct the international introduction and local diversification of RSV-A and RSV-B lineages, explore potential associations between variants and clinical severity, and prioritize mutations of potential relevance to immune escape using model based analysis. This work establishes a genomic baseline for RSV in a major international hub and illustrates how integrated epidemiological and evolutionary surveillance can support RSV monitoring in the prevention era. Methods Surveillance design Patients fulfilling the criteria for SARI/ILI/ARI were identified in the sentinel sites across the country, and diagnostic clinical specimens were sent to the National Influenza Center (NIC)-UAE and The Reference Laboratory for Infectious Diseases-Abu Dhabi (RLID-AD) for molecular identification of leading respiratory pathogens. SARI is defined as an acute respiratory infection in cases presented with fever (≥38°C) and cough, starting within the last 10 days, requiring hospitalization. ARI includes cases presented with the sudden onset of symptoms like cough, sore throat, shortness of breath, and coryza, with or without fever. 19 Epidemiological and clinical data are collected along with the collection of biological specimens such as nasal swabs, nasopharyngeal swabs, nasopharyngeal aspirates, nasal wash, and sputum as per the guidelines of the Global Influenza Surveillance and Response System (GISRS). If the patient was intubated, endotracheal aspirates or bronchoalveolar lavage were collected. The schematic diagram provided in the supplementary outlines the workflow, including the ARI and SARI processes (Fig. S1). Epidemiologic data analysis RSV case counts were visualized as monthly case counts stratified by severity. Severity was classified as an ordinal variable with three levels: Acute, which met the World Health Organization (WHO) definition for ARI, admitted to account for SARI cases, and ICU for patients who required ICU. Patients were stratified into five age categories: over 55, 19 - 55, 5 - 18, 1 - 4, and under 1 year old. The population burden of hospital admission was estimated by applying a Poisson regression, and children under 5 were considered as a single group for this analysis. To assess the association between age and severe disease outcomes, an ordinal logistic regression was applied, which adjusted for year to account for annual fluctuations and the impact of the COVID-19 pandemic. Specimen processing and laboratory detection of pathogens Nucleic acid was extracted from clinical respiratory samples using the STARMag Universal Cartridge Kit (Seegene, Korea) with an automated extraction system, according to the manufacturer’s instructions. Pathogen detection was conducted with the Allplex™ Respiratory Panel Assays (Seegene, Korea), a multiplex one-step real-time RT-PCR assay, using the Bio-Rad CFX 96 system (Bio-Rad Laboratories, California, USA). This assay identifies 26 respiratory pathogens, including 16 viruses and 7 bacteria. Genome Sequencing Whole genome sequencing (WGS) of RSV-positive samples with a Ct value of <28 was performed using next-generation sequencing (NGS) techniques. Whole genome amplification was conducted using PCR primer pools for RSV. 20 Library preparation for RSV samples was performed separately using the SQK-109 ligation sequencing kit (Oxford Nanopore Technologies, ONT) and multiplexed with the native barcoding kit EXPNBD104. Genome Assembly We conducted reference-based genome assembly using NanoCaller 3.6.2, optimized for high-accuracy variant calling from long-read data. 21 To generate a consensus genome sequence, we employed bcftools consensus with the --missing N option. 22 Candidate references were compared using genome-wide coverage profiles generated with a custom Python script (maskLowCov.py). EPI_ISL_1653999 and KT992094.1 were selected as the final assembly references for RSV-B and RSV-A, respectively. Contextualization and Phylogenetic analysis To investigate the phylogenetic relationships of local RSV strains within a global context, we employed the Nextstrain Augur pipeline. 23 This analysis integrated both locally generated sequences and publicly available global data to reconstruct time-resolved phylogenetic trees and infer geographic origins of transmission events. A total of 12,832 RSV-A and 10,113 RSV-B genome sequences were retrieved from the GISAID EpiRSV database. 24 Sequences were filtered to meet stringent quality control (QC) criteria, including genome coverage, Appropriate genome length per subtype, and completeness of critical metadata. These high-quality sequences were used to construct a representative global dataset for phylogenetic inference and contextualization of local strains. Local and contextual genome sequences were concatenated and aligned using augur align , which invokes MAFFT for multiple sequence alignment and trims insertions relative to a provided reference genome. 25 A maximum likelihood phylogenetic tree was constructed using the augur tree module, which internally invokes IQ-TREE for robust tree inference based on the aligned RSV sequences. 26 To incorporate temporal information and estimate evolutionary dynamics, the tree was further refined using augur refine , which calls TreeTime. 27 This refinement step infers divergence times, ancestral states, and geographic origin probabilities for internal nodes across the phylogeny. To improve the accuracy of temporal inference and mitigate the effects of sequencing artifacts, we applied the --clock-filter-iqd parameter during refinement. This option automatically prunes branches exhibiting abnormally high substitution rates, which are typically indicative of poor-quality sequences or technical errors. This filtering step enhances the reliability of inferred transmission routes and aids in distinguishing between global introductions and local transmission events. Phylogenetic Tree Annotation and Visualization Following tree construction and temporal refinement, internal nodes and tips were annotated with metadata including clade assignment, amino-acid substitutions, and ancestral country-state probabilities. These annotations supported interpretation of mutation dynamics, clustering patterns, and phylogeographic transitions in a global context. The contextual dataset, comprising more than 10,000 non-UAE RSV genomes, provided a broad phylogenetic framework in which UAE sequences could be analysed relative to globally sampled diversity. Ancestral country-state reconstruction was performed using augur traits, which generated node-level country assignments and posterior confidences. These annotations, together with branch length, nucleotide mutation, amino-acid mutation, and sample metadata, were incorporated into a Nextstrain-compatible JSON file using augur export v2. Although Auspice provides interactive exploration of phylogenetic trees and associated traits, it does not support all customized displays required for this study. We therefore developed a custom Python script (augur2itol.py) to convert Auspice JSON files into iTOL-compatible datasets, enabling integration of metadata, clade annotations, and antigenic-site mutation summaries for publication-quality visualization. For focused visualization of local diversity, subtype-specific RSV-A and RSV-B local phylogenies were generated by pruning the global trees with a custom Python script (prune2local.py) implemented with the ete3 library.28 This approach preserved the global tree topology while restricting the displayed view to UAE-associated sequences and their relevant phylogenetic context. Phylogeographic import and export analyses were performed on the subtype-specific RSV-A and RSV-B Nextstrain trees using node-level ancestral country-state reconstruction. A UAE introduction edge was defined as a parent-child transition in which the parent node was assigned to a non-UAE country and the child node was assigned to the UAE, whereas a UAE export edge was defined as a parent-child transition in which the parent node was assigned to the UAE and the child node was assigned to a non-UAE country. For both edge types, the maximum-posterior country assignments of the parent and child nodes were required to have posterior support of at least 0.90. Reported counts therefore represent minimum posterior-supported transition edges in the sampled phylogeny rather than absolute numbers of epidemiological importation or exportation events. RSV F-gene sequencing and mutation analysis Raw nanopore sequencing reads from clinical RSV samples were subjected to quality and length filtering using NanoFilt (v2.8.0) with parameters -q 7 -l 300, retaining only reads ≥300 bp with a mean quality score ≥7. Filtered reads were processed through a custom analysis workflow. The F-protein coding sequences from reference isolates RSV A-NLD-13-005275 (GenBank accession KX858757.1) and RSV B-NLD-13-001273 (KX858756.1) served as reference templates. 29 Reference sequences were indexed using samtools (v1.20) and minimap2 (v2.26; -d option). 22,30 Filtered reads were independently aligned to the RSV-A and RSV-B reference F-gene coding sequences using minimap2 (-ax map-ont, 8 threads). Alignments were converted to BAM format with samtools view -F 4, retaining only mapped reads. Each sample was provisionally typed as RSV-A or RSV-B according to the reference yielding the higher number of mapped reads; ties were assigned as undetermined and excluded from downstream analyses. Reads from typed samples were re-aligned to the corresponding reference sequence, sorted and indexed with samtools sort and samtools index. Coverage depth across the F-gene was computed using samtools depth (-aa -d 0), and mean depth, reference length, and coverage fraction were recorded per sample. Variant calling was performed using bcftools (v1.20). 22 Pileups were generated with bcftools mpileup (--min-MQ 20 --min-BQ 20 -d 0 --annotate FORMAT/DP), followed by variant detection with bcftools call -m -v --ploidy 1. Variants were stringently filtered to retain only those with QUAL ≥ 100 and FORMAT/DP ≥ 20. To avoid artefacts from poorly covered regions, bases with depth < 20 were masked to “N” using a BED mask generated from coverage profiles. Consensus F-gene coding sequences were reconstructed with bcftools consensus (-H A -m mask.bed), applying only filtered SNPs (indels excluded to prevent frameshift propagation). Each consensus CDS was translated into the corresponding amino-acid sequence using EMBOSS transeq (-frame 1 -clean). 31 Functional annotation of filtered variants was conducted using SnpEff (v5.2), and amino-acid substitutions were extracted from the resulting annotated VCF files. 32 Consensus-based protein sequences were further screened to exclude sequences with >2% ambiguous bases (“N”) or premature stop codons. High-quality consensus proteins (coverage fraction ≥ 0.98, mean depth ≥ 20×) were compared to the respective reference proteins to identify amino-acid substitutions. To place clinical isolates in global context, publicly available RSV F-gene sequences (collected 2021-2024) were retrieved from curated databases ( https://nextstrain.org/rsv/a/genome/6y ; accessed December 2025). Public metadata and nucleotide FASTA files for RSV-A and RSV-B were filtered for records with ≥95% F-gene coverage and valid sampling dates. F-gene coding sequences were extracted from full genomes by MAFFT (v7.520; --localpair --quiet) alignment to the reference F-gene CDS, and translated to protein. Sequences shorter than 95% of the reference F-gene or F-protein length were excluded. Amino-acid mutations were identified by comparison to the corresponding reference protein, using both forward and reverse-complement extraction where necessary. Mutation frequencies were computed for both UAE clinical isolates (meeting coverage ≥ 0.98 and mean depth ≥ 20×) and public sequences (coverage ≥ 0.95) and summarized by country and RSV type. Per-country, per-type frequencies were calculated as the proportion of samples containing a given mutation among all valid samples in that group. To visualize geographic and antigenic patterns, mutation frequency matrices were used to generate per-type heatmaps (RSV-A and RSV-B). Only mutations detected in ≥2 UAE strains were retained. Public countries with ≥30 valid samples were included for comparison. Annotated site maps highlighting known F-protein antigenic and antibody-binding regions (Sites Ø–V, P27 fusion peptide, Nirsevimab and Palivizumab epitopes) were overlaid to contextualize amino-acid variability across the protein. 11 Clinical association analyses of F-protein substitutions Clinical association analyses were performed separately for RSV-A and RSV-B using sample-level clinical metadata, the F-protein amino-acid mutation matrix, and subtype assignment and coverage metrics. Analyses were restricted to subtype-assigned samples with high-quality F-gene consensus sequences, defined by mean depth ≥20× and F-gene coverage fraction ≥0.98. The primary binary outcome was severe acute respiratory infection (SARI) versus acute respiratory infection (ARI). ICU status was not modelled as a separate endpoint in these mutation-level analyses because the clinical severity variable available for sequence-linked samples encoded only ARI/SARI status; accordingly, ICU cases contributed only through their recorded ARI or SARI classification. Mutation-level severity associations were first screened using two-sided Fisher’s exact tests. Age distribution analyses compared mutation frequencies between children aged 0 to 5 years and older patients using Fisher’s exact test, and compared age group midpoints using a two sided Mann-Whitney U test. To estimate adjusted severity associations under sparse counts, mutation-specific Firth penalized logistic regression models were fitted in R v4.3.3 using the logistf package (v1.26.1) 33 , with SARI as the dependent variable and age, sex, nationality, and city as covariates. Odds ratios, 95% confidence intervals, and two-sided P values were reported. Antigenic Escape analysis EVEscape analyses were performed separately for RSV-A F and RSV-B F using subtype-specific EVE models trained on public-only F-protein alignments containing 12,108 RSV-A sequences collected from 1956 to 2025 (11,747 with known collection year) and 9,976 RSV-B sequences collected from 1962 to 2025 (9,740 with known collection year), together with subtype reference sequences for alignment context. 34 For each possible non-reference amino-acid substitution across the 574-residue F protein, the adapted EVEscape workflow combined three components: an EVE-derived evolutionary fitness term, a structural accessibility term estimated from weighted contact number in subtype-matched prefusion RSV F structures 5UDC for RSV-A and 5UDD for RSV-B, and an amino-acid dissimilarity term. Single-substitution scores were converted to within-subtype percentiles relative to all 10,906 scored non-reference substitutions in the same subtype (574 positions × 19 amino-acid changes). The structural term was mapped directly to 448 of 574 RSV-A positions and 449 of 574 RSV-B positions. For variant-level UAE-versus-public comparisons, each UAE or public variant was summarized by the mean single-mutation EVEscape percentile across all substitutions in that variant. UAE variants were matched first by collection year and then by substitution count relative to the subtype-specific reference protein. Cohort-level P values were calculated using a two-sided stratum-summed Mann-Whitney U test across collection-year-by-substitution-count strata, with tie-corrected normal approximation. Sensitivity analyses were repeated after excluding USA sequences, excluding USA and France, and using country-balanced capped sampling. Results 4.1 Epidemiologic characteristics of RSV in the UAE We analyzed 2,350 laboratory-confirmed RSV infections recorded between 2018 and 2023. These cases comprised 458 (19.5%) acute respiratory infections (ARI), 1,278 (54.4%) hospital admissions with severe acute respiratory infection (SARI), and 614 (26.1%) admissions requiring intensive care (ICU). Notably, systematic testing for ARI cases only began in 2020, whereas SARI cases were routinely tested throughout the study period. A significant difference in age distribution was observed across severity cohorts (p < 0.001). The burden of severe disease fell overwhelmingly on the youngest children. The median age for patients with acute ARI was 8 years, which dropped to 1 year for SARI admissions and 0.6 years for ICU cases. Infants under one year bore a disproportionate burden, accounting for nearly half of SARI and almost 60% of ICU cases. Overall, children under five years represented 95% of all SARI and ICU admissions. Consistent with this pattern, ordinal logistic regression showed that infants younger than 1 year and children aged 1 to 4 years had significantly higher odds of more severe disease than the 5 to 18 year reference group (Fig. 1A, inset). Temporally, seasonal RSV peaks were driven almost entirely by admissions and ICU cases in children under five (Fig. 1A). A marked decline in RSV-associated hospitalizations occurred in 2020, coinciding with COVID-19 non-pharmaceutical interventions. This was followed by a sharp resurgence in hospitalizations in 2021 (Fig. 1B), which reached pre-pandemic levels. On a per capita basis, the annual incidence of RSV admissions was highest in 2021 (Fig. 1B). The highest burden of both admission and ICU admission was consistently and overwhelmingly observed in children under five, whose admission rates were orders of magnitude higher than all other age groups (Fig. 1C, D). Adults over 55 represented the group with the second-highest per capita admission rate. These data underscore that the most significant burden of severe, life-threatening RSV disease is concentrated in infants and young children. These findings reinforce the need for prevention strategies that directly protect infants and young children, alongside continued surveillance of severe pediatric RSV disease. 7 4.2 Genomic epidemiology and evolution of RSV in the UAE Our genomic surveillance identified co-circulation of both RSV-A and RSV-B in the UAE, with substantial diversity among locally sampled lineages (Fig. 2A, Fig. S2 and Table S1). Within RSV-B, clades B.D.4.1.1 and B.D.E.1 were present concurrently (Fig. 2A). Similarly, the local RSV-A population comprised multiple distinct clades, including A.D.1, A.D.5.1, A.D.3, and A.D.3.3 (Fig. S2). This broad contemporaneous diversity is consistent with repeated introductions of globally circulating RSV lineages rather than sustained circulation of a single endemic lineage. Metadata linked to these local phylogenies further supported the epidemiological patterns, with sequenced RSV-A and RSV-B infections concentrated in children aged 0 to 5 years, particularly among cases classified as SARI (Fig. 2A and Fig. S2). Together with the age-stratified admission patterns shown in Fig. 1C and Fig. 1D, these findings indicate that infants and young children represented the dominant age group among sequenced infections and severe disease in the cohort. To investigate the evolutionary dynamics of individual introductions, we examined a time-scaled phylogeny of a B.D.E.1 sublineage in a broader international context (Fig. 2B). This analysis is consistent with introduction of the lineage into the UAE in early 2023, with the ancestral virus carrying asparagine at F residue 466 (green branch). Subsequently, the F:N466S substitution emerged within the UAE lineage, giving rise to a derived sublineage around July 2023. This N466S-bearing lineage then diversified locally and was detected in eight UAE cases. The phylogenetic placement of this cluster is consistent with local emergence of F:N466S after introduction rather than importation of an already established N466S lineage. Closely related ancestral and contemporary international sequences, including strains from Canada, France, England, and Australia shown in Fig. 2B, retained the ancestral N466 state. F:N466S is also notable because it lies adjacent to the site IV region of the F protein (Fig. 3A). All eight UAE genomes within this lineage were sampled from children aged 0 to 5 years classified as SARI, supporting continued surveillance and follow-up evaluation of this lineage. 4.3 Mutational analysis and model based escape prioritization of the RSV F protein Applying stringent quality control criteria to F-gene sequence data, including coverage fraction ≥0.98 and mean depth ≥20×, yielded 312 high-quality isolates for downstream analysis, comprising 251 RSV-B and 61 RSV-A sequences. Among RSV-B isolates, the F protein displayed a heterogeneous mutational landscape. High-frequency substitutions were concentrated within the nirsevimab-associated site Ø region, particularly I206M, Q209R, and S211N, broadly mirroring global frequency patterns (Fig. 3A-B). By contrast, several lower-frequency substitutions were comparatively enriched in the UAE cohort, including F:V127I, F:V220I, F:G329E, and F:L467F, each observed at low frequency locally but remaining rare in most other sampled regions (Fig. 3A). At the variant level, UAE RSV-B sequences showed a marked upward shift in model-predicted EVEscape burden relative to year- and substitution-count-matched public RSV-B sequences (year × substitution-count stratified rank-sum P = 1.389 × 10 -24 ; Fig. 3C). The median matched percentile rank was 78.1% across all RSV-B variants and 82.4% among the non-reference variants, indicating that UAE RSV-B variants generally had higher variant-level mean EVEscape scores than most public RSV-B variants matched for collection year and substitution count. This enrichment remained detectable after excluding USA-derived public sequences (P = 1.235 × 10 -3 ), excluding both USA and France (P = 4.385 × 10 -3 ), and under country-balanced capped sampling (P = 4.023 × 10 -8 ), despite strong country imbalance in the original comparator set, which was dominated by USA sequences (1,543 of 1,839; 83.9%) with a smaller secondary contribution from France (83 of 1,839; 4.5%) (Fig. 3C). In RSV B, the UAE signal of elevated model predicted escape burden resolved into two lineage specific patterns within the dominant GB5.0.5a background (Fig. S3). In B.D.E.1, recurrent derived backgrounds carrying S173L, N466S, N116S, or V220I showed higher matched public percentile ranks than the recurrent core backbone. A similar pattern was observed in B.D.4.1.1, where derived backgrounds carrying A103I, V127I, or G329E also showed higher matched public percentile ranks than the corresponding core backbone. These findings indicate that the RSV B signal was concentrated in a subset of recurrent derived backgrounds rather than distributed uniformly across recurrent substitutions. Clinical association analyses identified subtype-specific RSV-B substitutions that may warrant follow-up. S211N showed an association with severe acute respiratory infection (SARI) in multivariable analysis (P = 0.0488), although this was not observed in univariate analysis (Fig. S4A-B). By contrast, F:N466S was observed only in SARI cases and was associated with younger age in univariate analyses (Fisher’s exact P = 0.0108; Mann-Whitney P = 0.0446), but its association with severity was not retained after adjustment (Fig. S4C-D). Overall, these findings support continued monitoring of N466S and S211N, while indicating that any relationship with clinical severity should be interpreted cautiously. In RSV-A, several substitutions, including F:A103T and F:T122A, were common across global datasets (Fig. 4A). Within the UAE cohort, L119H and A518V were underrepresented among SARI cases in univariate analyses (Fisher’s exact P = 0.0053; Fig. S5), but these findings were not retained after multivariable adjustment. Time-scaled phylogenetic analysis also identified UAE lineages emerging between 2023 and 2024, including a distinct subclade defined by F:L381F in antigenic site I (Fig. 4B). This substitution was first observed in the UAE in October 2023 and was also detected in three UK samples from a phylogenetically distant clade during the same month, consistent with convergent evolution at this site. In the RSV-A F protein, recurrent UAE substitutions were concentrated at relatively few sites and generally ranked lower on the single-mutation EVEscape scale than the prioritized RSV-B mutations (Fig. 4C). L119H was the most recurrent RSV-A substitution (n = 14), whereas L381F combined recurrence (n = 4) with the highest single-mutation EVEscape percentile among recurrent RSV-A substitutions (8.7%); K419E was also observed in four UAE variants but ranked lower (3.7%). Across both subtypes, the clesrovimab binding region in site IV remained highly conserved in UAE and global sequences. However, RSV-B carried substitutions adjacent to this region, including L467F and N466S (Fig. 3A). Overall, these findings integrate mutation frequency patterns, cohort-based clinical associations, and model-based escape prioritization to identify RSV variants that warrant continued surveillance and follow-up evaluation. 4.4 Global transmission of RSV strains to and from the UAE We placed UAE genomes in subtype-specific global phylogenies comprising 12,832 non-UAE RSV-A sequences and 10,113 non-UAE RSV-B sequences and used ancestral country-state reconstruction to identify posterior-supported geographic transitions involving the UAE (Fig. 5). Introduction and export events were defined as direct parent-child country-state transitions involving the UAE and were retained only when the maximum-posterior country assignments of both the parent and child nodes were at least 0.90. Under this criterion, we inferred a minimum of 10 RSV-A and 40 RSV-B introduction edges into the UAE, compared with 1 RSV-A and 22 RSV-B export edges from the UAE. Posterior-supported RSV-A introductions were distributed across multiple partner countries, including the United Kingdom, the United States, China, France, Ivory Coast, and South Africa, whereas RSV-B introductions were concentrated in the United Kingdom and the United States. Posterior-supported RSV-B export edges were less frequent than introductions and were most often linked to the United States and Qatar. These findings indicate that RSV circulation in the UAE during the study period was shaped primarily by repeated international introductions, particularly for RSV-B, with fewer posterior-supported export transitions in the sampled dataset. Discussion RSV remains a substantial public health challenge in the UAE, with the clinical burden concentrated disproportionately in infants and young children. National surveillance data showed that children younger than 5 years had the highest rates of hospital admission and ICU admission, and age was strongly associated with increasing disease severity. RSV circulation was also disrupted during the COVID-19 period, with reduced hospitalizations in 2020 followed by resurgence after relaxation of non-pharmaceutical interventions. Together, these findings highlight the need for prevention strategies that directly protect young children, including maternal and infant-focused approaches, alongside continued surveillance to monitor changes in RSV burden over time. 7 Our genomic and clinical analyses indicate that RSV circulation in the UAE during 2023 to 2024 was shaped primarily by repeated international introductions followed by local transmission. The placement of UAE isolates across the global RSV-A and RSV-B phylogenies, together with ancestral-state reconstruction, identified numerous distinct introduction events for both subtypes. 27,35 This pattern is consistent with the UAE functioning as a highly connected surveillance setting in which genetically diverse RSV lineages are repeatedly introduced and sampled. The epidemiological rebound observed after the pandemic is therefore accompanied by substantial viral diversity, especially in the pediatric population that bears the greatest clinical burden. 1 These findings carry particular significance in the current prevention era. Licensed vaccines for older adults and during pregnancy, alongside long-acting monoclonal antibodies for infants, predominantly target the prefusion F glycoprotein. 36 In our dataset, RSV-B substitutions clustered within or near site Ø, consistent with broader global circulation patterns. At the same time, the UAE dataset also contained lower-frequency mutations that were comparatively enriched locally. These included the N466S-bearing RSV-B cluster and the L381F-defined RSV-A subclade. Together, these findings show that surveillance in a globally connected setting can capture both dominant international trends and locally enriched variation that may warrant continued monitoring and follow-up evaluation. Model-based escape prioritization added a complementary layer of interpretation. UAE RSV-B variants showed a clear upward shift in variant-level EVEscape burden relative to year- and substitution-count-matched public comparators, and this signal remained detectable in sensitivity analyses addressing country imbalance in the comparator set. Notably, the RSV-B signal was not explained by the most common recurrent substitutions alone, but instead reflected a subset of recurrent and outlier variants, including V220I, G329E, and I542M, that ranked highly in the combined recurrence and EVEscape framework. These findings support the use of model-based prioritization to triage variants for surveillance and follow-up. However, they should not be interpreted as direct evidence of functional immune escape in the absence of neutralization or other experimental validation. At the same time, our results argue against interpreting any single substitution near a therapeutic epitope as evidence of reduced protection. Recent surveillance and experimental studies indicate that several prevalent RSV-B substitutions at or near the nirsevimab-associated region remain compatible with preserved susceptibility. 29,38 In parallel, we observed strong conservation of the clesrovimab-associated site IV region across UAE and contextual global sequences. These results support continued epitope-resolved surveillance across multiple antibody-binding regions, rather than focusing on single substitutions in isolation. 39,40 Several limitations should be acknowledged. Uneven geographic sampling in public databases may have influenced phylogeographic reconstruction and the inferred number and origin of introduction events. In addition, some comparative analyses relied on F-gene sequences rather than whole genomes, which may modestly limit phylogenetic resolution for fine-scale transmission patterns. Mutation-level clinical association analyses were also subject to residual confounding and limited statistical power for rare variants. Finally, the escape analyses were computational and not complemented here by functional neutralization or virological assays. As such, the prioritized variants identified in this study are best considered as candidates for further investigation rather than definitive escape or virulence markers. Despite these limitations, the convergence of epidemiological, phylodynamic, clinical, and model-based analyses provides a coherent picture of RSV circulation in a globally connected setting. The UAE captured repeated introductions of diverse RSV lineages, substantial local transmission, and a mutational landscape that included both globally prevalent and locally enriched variants. More broadly, this study illustrates how integrated genomic surveillance can identify RSV variants that warrant continued monitoring and experimental evaluation, and can provide early warning of antigenic shifts that may alter susceptibility to vaccines or monoclonal antibodies before such effects are evident clinically. As prevention strategies expand, these findings argue for genomic epidemiology to become a routine component of RSV surveillance. Declarations Data availability All raw sequencing data generated in this study are available in NCBI under BioProject accession PRJNA1449189. Code availability Code used for data processing and analysis is deposited at https://github.com/hz424/rsv_global. Acknowledgments We acknowledge the invaluable contributions of hospitals at surveillance sentinel sites, including Sheikh Shakhbout Medical City (SSMC) hospital, Mediclinic Hospital, Kanad Hospital, Tawam Hospital, and Al Ain Hospital. Their dedication to accurate data collection has been a cornerstone of this study's success. We sincerely thank the Reference Laboratory for Infectious Diseases (RLID-AD) team, part of Pure Lab, for their dedication and support throughout the study period. We gratefully acknowledge all data contributors, i.e., the Authors and their originating laboratories responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. 24 HZ is funded by Khalifa University FSU Grant 8474000820. Ethical Approval DOH/CVDC/2023/511 Conflict of interest Authors have no conflicts of interest to declare. Author contributions SMA, PM, MH, FAH, and FAA designed and implemented the surveillance, while DE, PM, FAH and SMA conceptualized the study. SMA, PM, FAH, and MH undertook data collection, cleaning, and management as well as specimen handling and laboratory protocols. The conceptualization of the study’s analysis was led by HZ, MS, AH, FAH, SMA and DE, and the statistical analysis was performed by MS and HZ, MS and AH carried out the bioinformatic analysis, with data visualization completed by MS, HZ and AH. The initial draft was prepared by HZ, MS, AH, and DE, and all authors participated in curating the drafts and provided final approval of the document. Abu Dhabi Sentinel Respiratory Surveillance Consortium Name Healthcare facility name Email Aarene Rennie Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Abdulla Fadhel Almehairbi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Ahlam Amer Al Maskari Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Amal Hasan M Abu Obaideh Sheikh Shakhbout Medical City [email protected] Ameera Al Shehhi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Amna Tamer Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Andreas Henschel Khalifa University, Abu Dhabi, United Arab Emirates [email protected] Anumol Surendhren Sheikh Shakhbout Medical City [email protected] Balqees Al Hayyas Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Bushra Abdulrahman Al Ghailani Baniyas Healthcare Center [email protected] Dean B Everett Khalifa University, Abu Dhabi, United Arab Emirates [email protected] Doaa Hussain Saad Elmelegy Baniyas Healthcare Center [email protected] Enan Abdalkareem Nawafleh Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Faisal Al Ahbabi Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Faouzi Ben Tijani Zarka Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Farida Al Hosani United Arab Emirates University [email protected] Fatima Hadi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Fouzia Jabeen PureLab, Abu Dhabi, UAE [email protected] Francis Amirtharaj Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE Clinical Microbiology and Immunology Laboratory, Research Laboratories, Khalifa University, Abu Dhabi, United Arab Emirates [email protected] Gamal Mohamed Hasan Ahmed Sheikh Shakhbout Medical City [email protected] Gracelin Vedmani Mediclinic Hospital airport road branch [email protected] Hala Imambaccus Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Jancy Varghese Sheikh Shakhbout Medical City [email protected] Jayalal Chellappan Sheikh Shakhbout Medical City [email protected] Jennifer Calapan Dequito Sheikh Shakhbout Medical City [email protected] Jherick Kagayutan Kanad Hospital jherick.kagayutan@kanadhospital .org Jocelyn Lacanaria Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Joselita Ladline Rego Sheikh Shakhbout Medical City [email protected] Kheir Mahmoud Abou elkheir Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Khulood Khaled Al Blooshi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Kiran Kumar Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Kristine Encelan Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Madikay Senghore Khalifa University, Abu Dhabi, United Arab Emirates [email protected] Mahra Al Hosani Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Maitha AlMansoori Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Mariam Rashed Al Saedi Al Muwaiji Health center [email protected] Marivic E. Astillero Kanad Hospital [email protected] rg Masitulah Nambalye Al Dhafra Family Medicine Center [email protected] Mauricio Paton Gasso Khalifa University, Abu Dhabi, United Arab Emirates [email protected] Monet Abraham Mediclinic Hospital airport road branch [email protected] Nael Sahhar Kanad Hospital [email protected] Pamela Fares Murad Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Pia Samson Kanad Hospital [email protected] Prameela Maniamma Sheikh Shakhbout Medical City [email protected] Ragi George Madinat Khalifa Healthcare Centre [email protected] Reham Jafer Al Hajjeh Sheikh Shakhbout Medical City [email protected] Revathi Angamuthu Al Muwaiji Health center [email protected] Robert B. Custodio Madinat Khalifa Healthcare Centre [email protected] Rosula Janelle Mallillin Al Dhafra Family Medicine Center [email protected] Sadeq Abdel Rahman Shehadeh Sheikh Shakhbout Medical City [email protected] Sahar Almarzooqi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Sajeed Abdulkader Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Salwa Mohamed Ali Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Salwa Mohamed Youssef Sheikh Shakhbout Medical City [email protected] Samina Yousaf Yousaf Al Dhafra Family Medicine Center [email protected] Santosh Abraham Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Sara Abdi Abu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates [email protected] Shaikha Jasim Al Zaabi Madinat Khalifa Healthcare Centre [email protected] Sheena Kabeer Sheikh Shakhbout Medical City [email protected] Siny Krishnan Sheikh Shakbout Medical City [email protected] Souby Pothen Al Muwaiji Health center [email protected] Stefan Weber PureLab, Abu Dhabi, UAE [email protected] Stephanie Kersi Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] Susan Abraham Al Muwaiji Health center [email protected] Susmitha Vijayan Baniyas Healthcare Center [email protected] Tarteel Abdallah Reference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE [email protected] References Fleming-Dutra, K. 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Structure-Based Design of a Fusion Glycoprotein Vaccine for Respiratory Syncytial Virus. Science 342 , 592–598 (2013). Sun, Y. et al. A potent broad-spectrum neutralizing antibody targeting a conserved region of the prefusion RSV F protein. Nat. Commun. 15 , 10085 (2024). Ngwuta, J. O. et al. Prefusion F–specific antibodies determine the magnitude of RSV neutralizing activity in human sera. Sci. Transl. Med. 7 , 309ra162-309ra162 (2015). Oraby, A. K. et al. A single amino acid mutation alters multiple neutralization epitopes in the respiratory syncytial virus fusion glycoprotein. Npj Viruses 3 , 33 (2025). Tables Table 1. Demographic and clinical characteristics of the study cohort. Comparison of patient demographics and clinical features across three distinct cohorts. P-values are derived from Chi-squared or Fisher's exact tests for categorical variables and Kruskal-Wallis test for continuous variables. RSV subtype categories are not mutually exclusive because some patients were positive for both RSV-A and RSV-B. Acute (N=458) Admitted (N=1278) ICU (N=614) p value Age < 0.001 Mean (SD) 17.165 (19.635) 2.544 (9.663) 2.005 (8.306) Median (Q1, Q3) 8.045 (3.415, 24.605) 1.000 (0.090, 2.000) 0.600 (0.080, 1.000) Age group < 0.001 Over 55 32 (7.0%) 19 (1.5%) 7 (1.1%) 19 - 55 101 (22.1%) 7 (0.5%) 2 (0.3%) 5 - 18 163 (35.6%) 51 (4.0%) 22 (3.6%) 1 - 4 141 (30.8%) 575 (45.0%) 217 (35.3%) Under 1 21 (4.6%) 626 (49.0%) 366 (59.6%) Sex < 0.001 NMiss 4 0 0 Female 243 (53.5%) 578 (45.2%) 248 (40.4%) Male 211 (46.5%) 700 (54.8%) 366 (59.6%) RSV.A 87 (19.0%) 501 (39.2%) 352 (57.3%) < 0.001 RSV.B 372 (81.0%) 787 (61.6%) 264 (43.0%) < 0.001 Pneumonia - 147 (11.5%) 50 (8.1%) < 0.001 Additional Declarations There is NO Competing Interest. Supplementary Files TableS1.xlsx Table S1 Supplementalmaterials.docx Supplemental_materials Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9344370","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622208297,"identity":"5c4e0311-d04c-4d5c-abf1-053fd0e06267","order_by":0,"name":"Hao Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACAxDB2MDAwM9DshbJHpK1GJwhVos5++EDjD932CRuPnPGgOFHDYM8PyEtlj1pCQySZ9ISt53tMWDsOcZgOLOBkMMO5BgwGLYdTtx2nseAgbeBgXHDAUJazr8xYEgEatncz2PA+LeBwX4/QS03gLYcBGrZwNtjwAy0JXEDIb8Y3HiWcLCxLc14xpljBYdljkkkzyDssOSDD3+22cj29yRvfPimxsa2v4GQNUBwAIkhQYT6UTAKRsEoGAUEAQBU0UJ0BOzVpAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7131-3758","institution":"Khalifa University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhou","suffix":""},{"id":622208298,"identity":"e76ec10a-cb20-494c-a4f7-d14504a8d0d0","order_by":1,"name":"Salwa Ali","email":"","orcid":"","institution":"Abu Dhabi Public Health Center","correspondingAuthor":false,"prefix":"","firstName":"Salwa","middleName":"","lastName":"Ali","suffix":""},{"id":622208299,"identity":"c1d14264-7c14-41ca-9d7a-404f3ad71538","order_by":2,"name":"Madikay Senghore","email":"","orcid":"","institution":"Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Madikay","middleName":"","lastName":"Senghore","suffix":""},{"id":622208300,"identity":"362d4e19-fd66-42d0-882d-06ee10f4fb95","order_by":3,"name":"Francis Amirtharaj","email":"","orcid":"","institution":"Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Amirtharaj","suffix":""},{"id":622208301,"identity":"7a62e9c9-baa2-4eef-890e-842d9fdb4637","order_by":4,"name":"Pamela Murad","email":"","orcid":"","institution":"Abu Dhabi Public Health Center","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Murad","suffix":""},{"id":622208302,"identity":"fabc9ad5-2c6c-4be1-a4ab-cb6c8656b966","order_by":5,"name":"Mahra Al Hosani","email":"","orcid":"","institution":"Abu Dhabi Public Health Center","correspondingAuthor":false,"prefix":"","firstName":"Mahra","middleName":"Al","lastName":"Hosani","suffix":""},{"id":622208303,"identity":"e753cc9a-373d-41f0-aed5-cfaec05086b8","order_by":6,"name":"Faisal Al Ahbabi","email":"","orcid":"","institution":"Abu Dhabi Public Health Center","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"Al","lastName":"Ahbabi","suffix":""},{"id":622208304,"identity":"8dfe32eb-681f-402b-b07d-b2decc05f05e","order_by":7,"name":"Andreas Henschel","email":"","orcid":"https://orcid.org/0000-0003-1386-5372","institution":"Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Henschel","suffix":""},{"id":622208305,"identity":"2f7fae5a-df3b-4cab-9971-4511ddceb3ef","order_by":8,"name":"Farida Al Hosani","email":"","orcid":"","institution":"Abu Dhabi Public Health Center","correspondingAuthor":false,"prefix":"","firstName":"Farida","middleName":"Al","lastName":"Hosani","suffix":""},{"id":622208306,"identity":"6572382b-55cf-4805-93ac-55185aee9eb1","order_by":9,"name":"Dean Everett","email":"","orcid":"","institution":"Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Dean","middleName":"","lastName":"Everett","suffix":""}],"badges":[],"createdAt":"2026-04-07 11:37:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9344370/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9344370/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106875997,"identity":"d603c905-5706-44b7-81a6-199bb7bb9272","added_by":"auto","created_at":"2026-04-14 10:27:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpidemiological trends and clinical severity of RSV infections, 2018–2023.\u003c/strong\u003e A) Monthly case counts from March 2018 to December 2023, stratified by clinical cohort (Acute, Admitted, Intensive care). Bar charts show case counts stacked by age group (Under 1, 1 to 4, 5 to 18, 19 to 55, Over 55). The inset forest plot shows the log odds of higher severity (95% CI) for each age group relative to the 5 to 18 year age group. B) Annual trends in total admissions and ICU admissions per 100,000 population from 2018 to 2022. C) Annual trends in admissions per 100,000 population from 2018 to 2022, stratified by age group. The y axis is shown on a logarithmic scale. D) Annual trends in ICU admissions per 100,000 population from 2018 to 2022, stratified by age group. The y axis is shown on a log scale. Shaded areas in panels B to D represent 95% confidence intervals. Note: Data for December 2023 were incomplete; panel A includes records only through 12 December 2023.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/0fc862a34ec679b95627d331.png"},{"id":106875983,"identity":"01c851aa-e250-428e-91aa-6a3162482ec6","added_by":"auto","created_at":"2026-04-14 10:27:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":529916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic context and clinical annotation of RSV-B genomes.\u003c/strong\u003e A) Maximum-likelihood phylogeny of RSV-B sequences with tip annotations for clade (B.D.4.1.1; B.D.E.1), clinical syndrome (ARI, acute respiratory infection; SARI, severe acute respiratory infection), sex, and age group. The heat map to the right marks selected F-protein amino acid substitutions; filled markers indicate presence of the substitution relative to the study reference. B) Time-scaled phylogeny with calendar time on the x-axis. Branch colour indicates genotype at F residue 466 (N or S), and tip symbols denote clinical syndrome (ARI or SARI). Tip labels indicate sampled locations of terminal lineages. Key lineage-defining changes along branches are annotated. ARI, acute respiratory infection; SARI, severe acute respiratory infection.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/311535536be37f5b6e529225.png"},{"id":106875996,"identity":"4eec490e-9c82-4f34-8a97-153bb4f14d5c","added_by":"auto","created_at":"2026-04-14 10:27:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3566773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRSV-B F-protein variation in the UAE identifies locally enriched substitutions, elevated model-predicted escape burden, and candidate severity-associated mutations\u003c/strong\u003e. A) Heatmap showing the frequency of F protein mutations in the UAE (N=251) compared to global datasets from 2021-2024. Antigenic sites for monoclonal antibodies are indicated on the right. B) Three-dimensional structure of the prefusion F protein trimer (PDB: 5UDD), with key antigenic sites colored. Mutations identified in the UAE cohort are mapped onto the structure as red spheres. C) Enrichment of UAE RSV-B variant-level EVEscape burden relative to matched public background. Each UAE RSV-B variant was summarized by the mean single-mutation EVEscape percentile across its substitutions, then ranked against public RSV-B variants matched by collection year and substitution count. The x axis shows the matched percentile threshold and the y axis shows the proportion of UAE variants at or above that threshold. The dashed grey line denotes the null expectation of no shift relative to matched public background, and the red curve shows the observed UAE distribution. P values were calculated using a two-sided stratum-summed Mann-Whitney U test across collection-year-by-substitution-count strata, with tie-corrected normal approximation. D) Mutation monitoring map for model-priority and surveillance mutations in RSV-B. Each point represents one amino-acid substitution observed in at least two UAE variants. The x axis reports UAE recurrence as the number of variants carrying that substitution, and the y axis reports the single-mutation EVEscape percentile relative to all 10,906 scored non-reference substitutions within that subtype (574 positions × 19 amino-acid changes). Point size reports mutation support within the escape-enriched variant tail of the primary variant-level analysis, defined as the number of carrier variants with matched public percentile ≥ 95%.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/6b70ce46f75784af5ffd45db.png"},{"id":106875985,"identity":"456cb0c7-24cb-473b-bc7d-7649fe08cec4","added_by":"auto","created_at":"2026-04-14 10:27:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":351276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRSV-A F-protein variation, convergent evolution, and model-based prioritization. \u003c/strong\u003eA) A heatmap displays the prevalence of amino acid substitutions in the RSV-A F-glycoprotein in sequences collected from selected countries between 2021 and 2024. The intensity of blue shading corresponds to the observed frequency of each mutation (rows) within the F-glycoprotein across different countries (columns). The accompanying bar chart on the right indicates the total number of unique sequences analyzed per country and visualizes the distribution of identified mutations across known antigenic sites and the F-protein cleavage site (F27/FP). B) A time-scaled maximum likelihood phylogenetic tree focusing on key global RSV-A F-glycoprotein lineages. This tree shows a time-scaled phylogenetic context for RSV-A variants defined by genotype at F residue 381 in relation to patient clinical outcomes (SARI: Severe Acute Respiratory Infection; ARI: Acute Respiratory Infection) and age categories (0-1 year, 1-5 years). The smaller inset phylogeny provides a broader evolutionary context of RSV-A globally from 2019-2024, with the main tree representing a zoomed-in perspective of a specific evolutionary period and clade. C) Mutation monitoring map for model-priority and surveillance mutations in RSV-A. Each point represents one amino-acid substitution observed in at least two UAE variants. The x axis reports UAE recurrence as the number of variants carrying that substitution, and the y axis reports the single-mutation EVEscape percentile relative to all 10,906 scored non-reference substitutions within that subtype (574 positions × 19 amino-acid changes). Point size reports mutation support within the escape-enriched variant tail of the primary variant-level analysis, defined as the number of carrier variants with matched public percentile ≥ 95%. Point color indicates antigenic site annotation.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/2330c1cf7c27b642bc98c323.png"},{"id":106875991,"identity":"bf0728ed-7530-4aaf-ac0f-1a2adf2e6011","added_by":"auto","created_at":"2026-04-14 10:27:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePosterior-filtered phylogeographic reconstruction of RSV transitions involving the UAE for RSV-A (left) and RSV-B (right). \u003c/strong\u003eChoropleth maps summarize inferred introductions into the UAE (left) and exports from the UAE (right) based on direct parent-child country-state transitions in global subtype-specific phylogenies. Local consensus genomes were contextualized against 12,832 RSV-A and 10,113 RSV-B non-UAE sequences. Geographic trait inference was performed using augur traits, and transitions were retained only when the maximum-posterior country assignments of both parent and child nodes were at least 0.90. For introductions, the partner country corresponds to the parent node; for exports, it corresponds to the child node. Map colour intensity indicates the number of posterior-supported transitions per country.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/42abab8594d5f8774aa0704c.png"},{"id":106876013,"identity":"7eaf4c4c-c962-4816-afeb-49ad0b9b48a4","added_by":"auto","created_at":"2026-04-14 10:28:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6114088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/89941d25-aab2-44c4-ae79-6870c72bfd0d.pdf"},{"id":106875980,"identity":"e7f3ea29-4296-40ed-81a8-07e77e762e30","added_by":"auto","created_at":"2026-04-14 10:27:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32883,"visible":true,"origin":"","legend":"Table S1","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/c6764ffe2f400c25f76ed709.xlsx"},{"id":106876011,"identity":"467b9338-3a04-45eb-bbf5-0db665d7639d","added_by":"auto","created_at":"2026-04-14 10:27:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2893658,"visible":true,"origin":"","legend":"Supplemental_materials","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9344370/v1/13a0e24431199f017f420fd5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genomic surveillance in the UAE reveals the global origins and local diversification of RSV lineages","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory Syncytial Virus (RSV) is a major global cause of acute lower respiratory tract infections (LRTIs), responsible for an estimated 33 million episodes and over 100,000 deaths in children under five annually.\u003csup\u003e1–4\u003c/sup\u003e After decades of challenges, the recent advent of prefusion F protein–based vaccines and long-acting monoclonal antibodies such as nirsevimab has heralded a new era in RSV prevention.\u003csup\u003e5,6\u003c/sup\u003e Maternal RSVpreF vaccination, nirsevimab administration in infants, and RSV vaccines for adults aged 60 years or older have each shown substantial protection, with pooled effectiveness estimates ranging from 68% to 83% against hospitalization across randomized and observational studies.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHowever, the long-term success of these interventions, which almost exclusively target the viral fusion (F) protein, is threatened by the virus's capacity for rapid antigenic drift.\u003csup\u003e8\u003c/sup\u003e This evolutionary pressure necessitates vigilant, genomics-informed surveillance to detect the emergence of variants that could escape neutralization and compromise these new public health tools.\u003csup\u003e9–12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAt the same time, the deployment of RSV vaccines and long-acting monoclonal antibodies has outpaced the establishment of genomic baselines in many under sampled regions. This creates an important surveillance gap: without contemporaneous data on lineage turnover, introduction dynamics, and the emergence of variants of potential relevance to surveillance and prevention, it is difficult to interpret how RSV populations are changing in the prevention era.\u003csup\u003e13\u003c/sup\u003e Genomically informed surveillance in globally connected settings is therefore needed not only to monitor mutations in therapeutic target proteins, but also to track how international viral movement and local transmission shape the diversity of circulating RSV populations.\u003csup\u003e14,15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhile surveillance is robust in some regions, the Middle East and North Africa (MENA), home to several major global civil aviation hubs, remains a critical blind spot in our understanding of global RSV circulation.\u003csup\u003e16\u003c/sup\u003e This data gap is particularly acute in nations that serve as major international crossroads, hindering the development of targeted public health interventions. The United Arab Emirates (UAE), a major node in global mobility and trade, provides a strategic vantage point for detecting the introduction and circulation of genetically diverse viral lineages originating from multiple geographic regions.\u003csup\u003e17,18\u003c/sup\u003e Understanding the molecular epidemiology of RSV in this context is therefore not just of local importance but provides a crucial window into global transmission dynamics.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we combined multi year epidemiological surveillance with genomic analysis of RSV positive clinical isolates collected in the UAE and placed these data in a global phylogenetic context. We sought to define the age and severity distribution of RSV burden, reconstruct the international introduction and local diversification of RSV-A and RSV-B lineages, explore potential associations between variants and clinical severity, and prioritize mutations of potential relevance to immune escape using model based analysis. This work establishes a genomic baseline for RSV in a major international hub and illustrates how integrated epidemiological and evolutionary surveillance can support RSV monitoring in the prevention era.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSurveillance design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients fulfilling the criteria for SARI/ILI/ARI were identified in the sentinel sites across the country, and diagnostic clinical specimens were sent to the National Influenza Center (NIC)-UAE and The Reference Laboratory for Infectious Diseases-Abu Dhabi (RLID-AD) for molecular identification of leading respiratory pathogens. SARI is defined as an acute respiratory infection in cases presented with fever (≥38°C) and cough, starting within the last 10 days, requiring hospitalization. ARI includes cases presented with the sudden onset of symptoms like cough, sore throat, shortness of breath, and coryza, with or without fever.\u003csup\u003e19\u003c/sup\u003e Epidemiological and clinical data are collected along with the collection of biological specimens such as nasal swabs, nasopharyngeal swabs, nasopharyngeal aspirates, nasal wash, and sputum as per the guidelines of the Global Influenza Surveillance and Response System (GISRS). If the patient was intubated, endotracheal aspirates or bronchoalveolar lavage were collected. The schematic diagram provided in the supplementary outlines the workflow, including the ARI and SARI processes (Fig. S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpidemiologic data analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRSV case counts were visualized as monthly case counts stratified by severity. Severity was classified as an ordinal variable with three levels: Acute, which met the World Health Organization (WHO) definition for ARI, admitted to account for SARI cases, and ICU for patients who required ICU. Patients were stratified into five age categories: over 55, 19 - 55, 5 - 18, 1 - 4, and under 1 year old. The population burden of hospital admission was estimated by applying a Poisson regression, and children under 5 were considered as a single group for this analysis. To assess the association between age and severe disease outcomes, an ordinal logistic regression was applied, which adjusted for year to account for annual fluctuations and the impact of the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen processing and laboratory detection of pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNucleic acid was extracted from clinical respiratory samples using the STARMag Universal Cartridge Kit (Seegene, Korea) with an automated extraction system, according to the manufacturer’s instructions. Pathogen detection was conducted with the Allplex™ Respiratory Panel Assays (Seegene, Korea), a multiplex one-step real-time RT-PCR assay, using the Bio-Rad CFX 96 system (Bio-Rad Laboratories, California, USA). This assay identifies 26 respiratory pathogens, including 16 viruses and 7 bacteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome Sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole genome sequencing (WGS) of RSV-positive samples with a Ct value of \u0026lt;28 was performed using next-generation sequencing (NGS) techniques. Whole genome amplification was conducted using PCR primer pools for RSV.\u003csup\u003e20\u003c/sup\u003e Library preparation for RSV samples was performed separately using the SQK-109 ligation sequencing kit (Oxford Nanopore Technologies, ONT) and multiplexed with the native barcoding kit EXPNBD104.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome Assembly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted reference-based genome assembly using NanoCaller 3.6.2, optimized for high-accuracy variant calling from long-read data.\u003csup\u003e21\u003c/sup\u003e To generate a consensus genome sequence, we employed \u003cem\u003ebcftools\u003c/em\u003e consensus with the \u003cem\u003e--missing N\u003c/em\u003e option.\u003csup\u003e22\u003c/sup\u003e Candidate references were compared using genome-wide coverage profiles generated with a custom Python script (maskLowCov.py). EPI_ISL_1653999 and KT992094.1 were selected as the final assembly references for RSV-B and RSV-A, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContextualization and Phylogenetic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the phylogenetic relationships of local RSV strains within a global context, we employed the Nextstrain Augur pipeline.\u003csup\u003e23\u003c/sup\u003e This analysis integrated both locally generated sequences and publicly available global data to reconstruct time-resolved phylogenetic trees and infer geographic origins of transmission events.\u003c/p\u003e\n\u003cp\u003eA total of 12,832 RSV-A and 10,113 RSV-B genome sequences were retrieved from the GISAID EpiRSV database.\u003csup\u003e24\u003c/sup\u003e Sequences were filtered to meet stringent quality control (QC) criteria, including genome coverage, Appropriate genome length per subtype, and completeness of critical metadata. These high-quality sequences were used to construct a representative global dataset for phylogenetic inference and contextualization of local strains.\u003c/p\u003e\n\u003cp\u003eLocal and contextual genome sequences were concatenated and aligned \u003cem\u003eusing augur align\u003c/em\u003e, which invokes MAFFT for multiple sequence alignment and trims insertions relative to a provided reference genome.\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA maximum likelihood phylogenetic tree was constructed using the \u003cem\u003eaugur tree\u003c/em\u003e module, which internally invokes IQ-TREE for robust tree inference based on the aligned RSV sequences.\u003csup\u003e26\u003c/sup\u003e To incorporate temporal information and estimate evolutionary dynamics, the tree was further refined using \u003cem\u003eaugur refine\u003c/em\u003e, which calls TreeTime.\u003csup\u003e27\u003c/sup\u003e This refinement step infers divergence times, ancestral states, and geographic origin probabilities for internal nodes across the phylogeny.\u003c/p\u003e\n\u003cp\u003eTo improve the accuracy of temporal inference and mitigate the effects of sequencing artifacts, we applied the \u003cem\u003e--clock-filter-iqd\u003c/em\u003e parameter during refinement. This option automatically prunes branches exhibiting abnormally high substitution rates, which are typically indicative of poor-quality sequences or technical errors. This filtering step enhances the reliability of inferred transmission routes and aids in distinguishing between global introductions and local transmission events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic Tree Annotation and Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing tree construction and temporal refinement, internal nodes and tips were annotated with metadata including clade assignment, amino-acid substitutions, and ancestral country-state probabilities. These annotations supported interpretation of mutation dynamics, clustering patterns, and phylogeographic transitions in a global context. The contextual dataset, comprising more than 10,000 non-UAE RSV genomes, provided a broad phylogenetic framework in which UAE sequences could be analysed relative to globally sampled diversity.\u003c/p\u003e\n\u003cp\u003eAncestral country-state reconstruction was performed using augur traits, which generated node-level country assignments and posterior confidences. These annotations, together with branch length, nucleotide mutation, amino-acid mutation, and sample metadata, were incorporated into a Nextstrain-compatible JSON file using augur export v2. Although Auspice provides interactive exploration of phylogenetic trees and associated traits, it does not support all customized displays required for this study. We therefore developed a custom Python script (augur2itol.py) to convert Auspice JSON files into iTOL-compatible datasets, enabling integration of metadata, clade annotations, and antigenic-site mutation summaries for publication-quality visualization.\u003c/p\u003e\n\u003cp\u003eFor focused visualization of local diversity, subtype-specific RSV-A and RSV-B local phylogenies were generated by pruning the global trees with a custom Python script (prune2local.py) implemented with the ete3 library.28 This approach preserved the global tree topology while restricting the displayed view to UAE-associated sequences and their relevant phylogenetic context.\u003c/p\u003e\n\u003cp\u003ePhylogeographic import and export analyses were performed on the subtype-specific RSV-A and RSV-B Nextstrain trees using node-level ancestral country-state reconstruction. A UAE introduction edge was defined as a parent-child transition in which the parent node was assigned to a non-UAE country and the child node was assigned to the UAE, whereas a UAE export edge was defined as a parent-child transition in which the parent node was assigned to the UAE and the child node was assigned to a non-UAE country. For both edge types, the maximum-posterior country assignments of the parent and child nodes were required to have posterior support of at least 0.90. Reported counts therefore represent minimum posterior-supported transition edges in the sampled phylogeny rather than absolute numbers of epidemiological importation or exportation events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRSV F-gene sequencing and mutation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw nanopore sequencing reads from clinical RSV samples were subjected to quality and length filtering using NanoFilt (v2.8.0) with parameters -q 7 -l 300, retaining only reads ≥300 bp with a mean quality score ≥7. Filtered reads were processed through a custom analysis workflow. The F-protein coding sequences from reference isolates RSV A-NLD-13-005275 (GenBank accession KX858757.1) and RSV B-NLD-13-001273 (KX858756.1) served as reference templates.\u003csup\u003e29\u003c/sup\u003e Reference sequences were indexed using samtools (v1.20) and minimap2 (v2.26; -d option).\u003csup\u003e22,30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFiltered reads were independently aligned to the RSV-A and RSV-B reference F-gene coding sequences using minimap2 (-ax map-ont, 8 threads). Alignments were converted to BAM format with samtools view -F 4, retaining only mapped reads. Each sample was provisionally typed as RSV-A or RSV-B according to the reference yielding the higher number of mapped reads; ties were assigned as undetermined and excluded from downstream analyses. Reads from typed samples were re-aligned to the corresponding reference sequence, sorted and indexed with samtools sort and samtools index. Coverage depth across the F-gene was computed using samtools depth (-aa -d 0), and mean depth, reference length, and coverage fraction were recorded per sample.\u003c/p\u003e\n\u003cp\u003eVariant calling was performed using bcftools (v1.20).\u003csup\u003e22\u003c/sup\u003e Pileups were generated with bcftools mpileup (--min-MQ 20 --min-BQ 20 -d 0 --annotate FORMAT/DP), followed by variant detection with bcftools call -m -v --ploidy 1. Variants were stringently filtered to retain only those with QUAL ≥ 100 and FORMAT/DP ≥ 20. To avoid artefacts from poorly covered regions, bases with depth \u0026lt; 20 were masked to “N” using a BED mask generated from coverage profiles. Consensus F-gene coding sequences were reconstructed with bcftools consensus (-H A -m mask.bed), applying only filtered SNPs (indels excluded to prevent frameshift propagation). Each consensus CDS was translated into the corresponding amino-acid sequence using EMBOSS transeq (-frame 1 -clean).\u003csup\u003e31\u003c/sup\u003e Functional annotation of filtered variants was conducted using SnpEff (v5.2), and amino-acid substitutions were extracted from the resulting annotated VCF files.\u003csup\u003e32\u003c/sup\u003e Consensus-based protein sequences were further screened to exclude sequences with \u0026gt;2% ambiguous bases (“N”) or premature stop codons.\u003c/p\u003e\n\u003cp\u003eHigh-quality consensus proteins (coverage fraction ≥ 0.98, mean depth ≥ 20×) were compared to the respective reference proteins to identify amino-acid substitutions. To place clinical isolates in global context, publicly available RSV F-gene sequences (collected 2021-2024) were retrieved from curated databases (\u003ca href=\"https://nextstrain.org/rsv/a/genome/6y\"\u003ehttps://nextstrain.org/rsv/a/genome/6y\u003c/a\u003e; accessed December 2025). Public metadata and nucleotide FASTA files for RSV-A and RSV-B were filtered for records with ≥95% F-gene coverage and valid sampling dates. F-gene coding sequences were extracted from full genomes by MAFFT (v7.520; --localpair --quiet) alignment to the reference F-gene CDS, and translated to protein. Sequences shorter than 95% of the reference F-gene or F-protein length were excluded. Amino-acid mutations were identified by comparison to the corresponding reference protein, using both forward and reverse-complement extraction where necessary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMutation frequencies were computed for both UAE clinical isolates (meeting coverage ≥ 0.98 and mean depth ≥ 20×) and public sequences (coverage ≥ 0.95) and summarized by country and RSV type. Per-country, per-type frequencies were calculated as the proportion of samples containing a given mutation among all valid samples in that group.\u003c/p\u003e\n\u003cp\u003eTo visualize geographic and antigenic patterns, mutation frequency matrices were used to generate per-type heatmaps (RSV-A and RSV-B). Only mutations detected in ≥2 UAE strains were retained. Public countries with ≥30 valid samples were included for comparison. Annotated site maps highlighting known F-protein antigenic and antibody-binding regions (Sites Ø–V, P27 fusion peptide, Nirsevimab and Palivizumab epitopes) were overlaid to contextualize amino-acid variability across the protein.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical association analyses of F-protein substitutions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical association analyses were performed separately for RSV-A and RSV-B using sample-level clinical metadata, the F-protein amino-acid mutation matrix, and subtype assignment and coverage metrics. Analyses were restricted to subtype-assigned samples with high-quality F-gene consensus sequences, defined by mean depth ≥20× and F-gene coverage fraction ≥0.98. The primary binary outcome was severe acute respiratory infection (SARI) versus acute respiratory infection (ARI). ICU status was not modelled as a separate endpoint in these mutation-level analyses because the clinical severity variable available for sequence-linked samples encoded only ARI/SARI status; accordingly, ICU cases contributed only through their recorded ARI or SARI classification. Mutation-level severity associations were first screened using two-sided Fisher’s exact tests. Age distribution analyses compared mutation frequencies between children aged 0 to 5 years and older patients using Fisher’s exact test, and compared age group midpoints using a two sided Mann-Whitney U test. To estimate adjusted severity associations under sparse counts, mutation-specific Firth penalized logistic regression models were fitted in R v4.3.3 using the logistf package (v1.26.1)\u003csup\u003e33\u003c/sup\u003e, with SARI as the dependent variable and age, sex, nationality, and city as covariates. Odds ratios, 95% confidence intervals, and two-sided P values were reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntigenic Escape analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEVEscape analyses were performed separately for RSV-A F and RSV-B F using subtype-specific EVE models trained on public-only F-protein alignments containing 12,108 RSV-A sequences collected from 1956 to 2025 (11,747 with known collection year) and 9,976 RSV-B sequences collected from 1962 to 2025 (9,740 with known collection year), together with subtype reference sequences for alignment context.\u003csup\u003e34\u003c/sup\u003e For each possible non-reference amino-acid substitution across the 574-residue F protein, the adapted EVEscape workflow combined three components: an EVE-derived evolutionary fitness term, a structural accessibility term estimated from weighted contact number in subtype-matched prefusion RSV F structures 5UDC for RSV-A and 5UDD for RSV-B, and an amino-acid dissimilarity term. Single-substitution scores were converted to within-subtype percentiles relative to all 10,906 scored non-reference substitutions in the same subtype (574 positions × 19 amino-acid changes). The structural term was mapped directly to 448 of 574 RSV-A positions and 449 of 574 RSV-B positions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor variant-level UAE-versus-public comparisons, each UAE or public variant was summarized by the mean single-mutation EVEscape percentile across all substitutions in that variant. UAE variants were matched first by collection year and then by substitution count relative to the subtype-specific reference protein. Cohort-level P values were calculated using a two-sided stratum-summed Mann-Whitney U test across collection-year-by-substitution-count strata, with tie-corrected normal approximation. Sensitivity analyses were repeated after excluding USA sequences, excluding USA and France, and using country-balanced capped sampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Epidemiologic characteristics of RSV in the UAE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed 2,350 laboratory-confirmed RSV infections recorded between 2018 and 2023. These cases comprised 458 (19.5%) acute respiratory infections (ARI), 1,278 (54.4%) hospital admissions with severe acute respiratory infection (SARI), and 614 (26.1%) admissions requiring intensive care (ICU). Notably, systematic testing for ARI cases only began in 2020, whereas SARI cases were routinely tested throughout the study period.\u003c/p\u003e\n\u003cp\u003eA significant difference in age distribution was observed across severity cohorts (p \u0026lt; 0.001). The burden of severe disease fell overwhelmingly on the youngest children. The median age for patients with acute ARI was 8 years, which dropped to 1 year for SARI admissions and 0.6 years for ICU cases. Infants under one year bore a disproportionate burden, accounting for nearly half of SARI and almost 60% of ICU cases. Overall, children under five years represented 95% of all SARI and ICU admissions. Consistent with this pattern, ordinal logistic regression showed that infants younger than 1 year and children aged 1 to 4 years had significantly higher odds of more severe disease than the 5 to 18 year reference group (Fig. 1A, inset).\u003c/p\u003e\n\u003cp\u003eTemporally, seasonal RSV peaks were driven almost entirely by admissions and ICU cases in children under five (Fig. 1A). A marked decline in RSV-associated hospitalizations occurred in 2020, coinciding with COVID-19 non-pharmaceutical interventions. This was followed by a sharp resurgence in hospitalizations in 2021 (Fig. 1B), which reached pre-pandemic levels.\u003c/p\u003e\n\u003cp\u003eOn a per capita basis, the annual incidence of RSV admissions was highest in 2021 (Fig. 1B). The highest burden of both admission and ICU admission was consistently and overwhelmingly observed in children under five, whose admission rates were orders of magnitude higher than all other age groups (Fig. 1C, D). Adults over 55 represented the group with the second-highest per capita admission rate. These data underscore that the most significant burden of severe, life-threatening RSV disease is concentrated in infants and young children. These findings reinforce the need for prevention strategies that directly protect infants and young children, alongside continued surveillance of severe pediatric RSV disease.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Genomic epidemiology and evolution of RSV in the UAE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur genomic surveillance identified co-circulation of both RSV-A and RSV-B in the UAE, with substantial diversity among locally sampled lineages (Fig. 2A, Fig. S2 and Table S1). Within RSV-B, clades B.D.4.1.1 and B.D.E.1 were present concurrently (Fig. 2A). Similarly, the local RSV-A population comprised multiple distinct clades, including A.D.1, A.D.5.1, A.D.3, and A.D.3.3 (Fig. S2). This broad contemporaneous diversity is consistent with repeated introductions of globally circulating RSV lineages rather than sustained circulation of a single endemic lineage. Metadata linked to these local phylogenies further supported the epidemiological patterns, with sequenced RSV-A and RSV-B infections concentrated in children aged 0 to 5 years, particularly among cases classified as SARI (Fig. 2A and Fig. S2). Together with the age-stratified admission patterns shown in Fig. 1C and Fig. 1D, these findings indicate that infants and young children represented the dominant age group among sequenced infections and severe disease in the cohort.\u003c/p\u003e\n\u003cp\u003eTo investigate the evolutionary dynamics of individual introductions, we examined a time-scaled phylogeny of a B.D.E.1 sublineage in a broader international context (Fig. 2B). This analysis is consistent with introduction of the lineage into the UAE in early 2023, with the ancestral virus carrying asparagine at F residue 466 (green branch). Subsequently, the F:N466S substitution emerged within the UAE lineage, giving rise to a derived sublineage around July 2023. This N466S-bearing lineage then diversified locally and was detected in eight UAE cases.\u003c/p\u003e\n\u003cp\u003eThe phylogenetic placement of this cluster is consistent with local emergence of F:N466S after introduction rather than importation of an already established N466S lineage. Closely related ancestral and contemporary international sequences, including strains from Canada, France, England, and Australia shown in Fig. 2B, retained the ancestral N466 state. F:N466S is also notable because it lies adjacent to the site IV region of the F protein (Fig. 3A). All eight UAE genomes within this lineage were sampled from children aged 0 to 5 years classified as SARI, supporting continued surveillance and follow-up evaluation of this lineage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Mutational analysis and model based escape prioritization of the RSV F protein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplying stringent quality control criteria to F-gene sequence data, including coverage fraction ≥0.98 and mean depth ≥20×, yielded 312 high-quality isolates for downstream analysis, comprising 251 RSV-B and 61 RSV-A sequences.\u003c/p\u003e\n\u003cp\u003eAmong RSV-B isolates, the F protein displayed a heterogeneous mutational landscape. High-frequency substitutions were concentrated within the nirsevimab-associated site Ø region, particularly I206M, Q209R, and S211N, broadly mirroring global frequency patterns (Fig. 3A-B). By contrast, several lower-frequency substitutions were comparatively enriched in the UAE cohort, including F:V127I, F:V220I, F:G329E, and F:L467F, each observed at low frequency locally but remaining rare in most other sampled regions (Fig. 3A).\u003c/p\u003e\n\u003cp\u003eAt the variant level, UAE RSV-B sequences showed a marked upward shift in model-predicted EVEscape burden relative to year- and substitution-count-matched public RSV-B sequences (year × substitution-count stratified rank-sum P = 1.389 × 10\u003csup\u003e-24\u003c/sup\u003e; Fig. 3C). The median matched percentile rank was 78.1% across all RSV-B variants and 82.4% among the non-reference variants, indicating that UAE RSV-B variants generally had higher variant-level mean EVEscape scores than most public RSV-B variants matched for collection year and substitution count. This enrichment remained detectable after excluding USA-derived public sequences (P = 1.235 × 10\u003csup\u003e-3\u003c/sup\u003e), excluding both USA and France (P = 4.385 × 10\u003csup\u003e-3\u003c/sup\u003e), and under country-balanced capped sampling (P = 4.023 × 10\u003csup\u003e-8\u003c/sup\u003e), despite strong country imbalance in the original comparator set, which was dominated by USA sequences (1,543 of 1,839; 83.9%) with a smaller secondary contribution from France (83 of 1,839; 4.5%) (Fig. 3C).\u003c/p\u003e\n\u003cp\u003eIn RSV B, the UAE signal of elevated model predicted escape burden resolved into two lineage specific patterns within the dominant GB5.0.5a background (Fig. S3). In B.D.E.1, recurrent derived backgrounds carrying S173L, N466S, N116S, or V220I showed higher matched public percentile ranks than the recurrent core backbone. A similar pattern was observed in B.D.4.1.1, where derived backgrounds carrying A103I, V127I, or G329E also showed higher matched public percentile ranks than the corresponding core backbone. These findings indicate that the RSV B signal was concentrated in a subset of recurrent derived backgrounds rather than distributed uniformly across recurrent substitutions.\u003c/p\u003e\n\u003cp\u003eClinical association analyses identified subtype-specific RSV-B substitutions that may warrant follow-up. S211N showed an association with severe acute respiratory infection (SARI) in multivariable analysis (P = 0.0488), although this was not observed in univariate analysis (Fig. S4A-B). By contrast, F:N466S was observed only in SARI cases and was associated with younger age in univariate analyses (Fisher’s exact P = 0.0108; Mann-Whitney P = 0.0446), but its association with severity was not retained after adjustment (Fig. S4C-D). Overall, these findings support continued monitoring of N466S and S211N, while indicating that any relationship with clinical severity should be interpreted cautiously.\u003c/p\u003e\n\u003cp\u003eIn RSV-A, several substitutions, including F:A103T and F:T122A, were common across global datasets (Fig. 4A). Within the UAE cohort, L119H and A518V were underrepresented among SARI cases in univariate analyses (Fisher’s exact P = 0.0053; Fig. S5), but these findings were not retained after multivariable adjustment. Time-scaled phylogenetic analysis also identified UAE lineages emerging between 2023 and 2024, including a distinct subclade defined by F:L381F in antigenic site I (Fig. 4B). This substitution was first observed in the UAE in October 2023 and was also detected in three UK samples from a phylogenetically distant clade during the same month, consistent with convergent evolution at this site. In the RSV-A F protein, recurrent UAE substitutions were concentrated at relatively few sites and generally ranked lower on the single-mutation EVEscape scale than the prioritized RSV-B mutations (Fig. 4C). L119H was the most recurrent RSV-A substitution (n = 14), whereas L381F combined recurrence (n = 4) with the highest single-mutation EVEscape percentile among recurrent RSV-A substitutions (8.7%); K419E was also observed in four UAE variants but ranked lower (3.7%).\u003c/p\u003e\n\u003cp\u003eAcross both subtypes, the clesrovimab binding region in site IV remained highly conserved in UAE and global sequences. However, RSV-B carried substitutions adjacent to this region, including L467F and N466S (Fig. 3A). Overall, these findings integrate mutation frequency patterns, cohort-based clinical associations, and model-based escape prioritization to identify RSV variants that warrant continued surveillance and follow-up evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Global transmission of RSV strains to and from the UAE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe placed UAE genomes in subtype-specific global phylogenies comprising 12,832 non-UAE RSV-A sequences and 10,113 non-UAE RSV-B sequences and used ancestral country-state reconstruction to identify posterior-supported geographic transitions involving the UAE (Fig. 5). Introduction and export events were defined as direct parent-child country-state transitions involving the UAE and were retained only when the maximum-posterior country assignments of both the parent and child nodes were at least 0.90. Under this criterion, we inferred a minimum of 10 RSV-A and 40 RSV-B introduction edges into the UAE, compared with 1 RSV-A and 22 RSV-B export edges from the UAE. Posterior-supported RSV-A introductions were distributed across multiple partner countries, including the United Kingdom, the United States, China, France, Ivory Coast, and South Africa, whereas RSV-B introductions were concentrated in the United Kingdom and the United States. Posterior-supported RSV-B export edges were less frequent than introductions and were most often linked to the United States and Qatar. These findings indicate that RSV circulation in the UAE during the study period was shaped primarily by repeated international introductions, particularly for RSV-B, with fewer posterior-supported export transitions in the sampled dataset.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRSV remains a substantial public health challenge in the UAE, with the clinical burden concentrated disproportionately in infants and young children. National surveillance data showed that children younger than 5 years had the highest rates of hospital admission and ICU admission, and age was strongly associated with increasing disease severity. RSV circulation was also disrupted during the COVID-19 period, with reduced hospitalizations in 2020 followed by resurgence after relaxation of non-pharmaceutical interventions. Together, these findings highlight the need for prevention strategies that directly protect young children, including maternal and infant-focused approaches, alongside continued surveillance to monitor changes in RSV burden over time.\u003csup\u003e7\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur genomic and clinical analyses indicate that RSV circulation in the UAE during 2023 to 2024 was shaped primarily by repeated international introductions followed by local transmission. The placement of UAE isolates across the global RSV-A and RSV-B phylogenies, together with ancestral-state reconstruction, identified numerous distinct introduction events for both subtypes.\u003csup\u003e27,35\u003c/sup\u003e This pattern is consistent with the UAE functioning as a highly connected surveillance setting in which genetically diverse RSV lineages are repeatedly introduced and sampled. The epidemiological rebound observed after the pandemic is therefore accompanied by substantial viral diversity, especially in the pediatric population that bears the greatest clinical burden.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThese findings carry particular significance in the current prevention era. Licensed vaccines for older adults and during pregnancy, alongside long-acting monoclonal antibodies for infants, predominantly target the prefusion F glycoprotein.\u003csup\u003e36\u003c/sup\u003e In our dataset, RSV-B substitutions clustered within or near site Ø, consistent with broader global circulation patterns. At the same time, the UAE dataset also contained lower-frequency mutations that were comparatively enriched locally. These included the N466S-bearing RSV-B cluster and the L381F-defined RSV-A subclade. Together, these findings show that surveillance in a globally connected setting can capture both dominant international trends and locally enriched variation that may warrant continued monitoring and follow-up evaluation.\u003c/p\u003e\n\u003cp\u003eModel-based escape prioritization added a complementary layer of interpretation. UAE RSV-B variants showed a clear upward shift in variant-level EVEscape burden relative to year- and substitution-count-matched public comparators, and this signal remained detectable in sensitivity analyses addressing country imbalance in the comparator set. Notably, the RSV-B signal was not explained by the most common recurrent substitutions alone, but instead reflected a subset of recurrent and outlier variants, including V220I, G329E, and I542M, that ranked highly in the combined recurrence and EVEscape framework. These findings support the use of model-based prioritization to triage variants for surveillance and follow-up. However, they should not be interpreted as direct evidence of functional immune escape in the absence of neutralization or other experimental validation.\u003c/p\u003e\n\u003cp\u003eAt the same time, our results argue against interpreting any single substitution near a therapeutic epitope as evidence of reduced protection. Recent surveillance and experimental studies indicate that several prevalent RSV-B substitutions at or near the nirsevimab-associated region remain compatible with preserved susceptibility.\u003csup\u003e29,38\u003c/sup\u003e In parallel, we observed strong conservation of the clesrovimab-associated site IV region across UAE and contextual global sequences. These results support continued epitope-resolved surveillance across multiple antibody-binding regions, rather than focusing on single substitutions in isolation.\u003csup\u003e39,40\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. Uneven geographic sampling in public databases may have influenced phylogeographic reconstruction and the inferred number and origin of introduction events. In addition, some comparative analyses relied on F-gene sequences rather than whole genomes, which may modestly limit phylogenetic resolution for fine-scale transmission patterns. Mutation-level clinical association analyses were also subject to residual confounding and limited statistical power for rare variants. Finally, the escape analyses were computational and not complemented here by functional neutralization or virological assays. As such, the prioritized variants identified in this study are best considered as candidates for further investigation rather than definitive escape or virulence markers.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the convergence of epidemiological, phylodynamic, clinical, and model-based analyses provides a coherent picture of RSV circulation in a globally connected setting. The UAE captured repeated introductions of diverse RSV lineages, substantial local transmission, and a mutational landscape that included both globally prevalent and locally enriched variants. More broadly, this study illustrates how integrated genomic surveillance can identify RSV variants that warrant continued monitoring and experimental evaluation, and can provide early warning of antigenic shifts that may alter susceptibility to vaccines or monoclonal antibodies before such effects are evident clinically. As prevention strategies expand, these findings argue for genomic epidemiology to become a routine component of RSV surveillance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData availability\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequencing data generated in this study are available in NCBI under BioProject accession PRJNA1449189.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCode availability\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode used for data processing and analysis is deposited at https://github.com/hz424/rsv_global.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the invaluable contributions of hospitals at surveillance sentinel sites, including Sheikh Shakhbout Medical City (SSMC) hospital, Mediclinic Hospital, Kanad Hospital, Tawam Hospital, and Al Ain Hospital. Their dedication to accurate data collection has been a cornerstone of this study\u0026apos;s success. We sincerely thank the Reference Laboratory for Infectious Diseases (RLID-AD) team, part of Pure Lab, for their dedication and support throughout the study period. We gratefully acknowledge all data contributors, i.e., the Authors and their originating laboratories responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based.\u003csup\u003e24\u003c/sup\u003e HZ is funded by Khalifa University FSU Grant 8474000820.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDOH/CVDC/2023/511\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no conflicts of interest to declare.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMA, PM, MH, FAH, and FAA designed and implemented the surveillance, while DE, PM, FAH and SMA conceptualized the study. SMA, PM, FAH, and MH undertook data collection, cleaning, and management as well as specimen handling and laboratory protocols. The conceptualization of the study\u0026rsquo;s analysis was led by HZ, MS, AH, FAH, SMA and DE, and the statistical analysis was performed by MS and HZ, MS and AH carried out the bioinformatic analysis, with data visualization completed by MS, HZ and AH. The initial draft was prepared by HZ, MS, AH, and DE, and all authors participated in curating the drafts and provided final approval of the document.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbu Dhabi Sentinel Respiratory Surveillance Consortium\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare facility name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmail\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAarene Rennie\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbdulla Fadhel Almehairbi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAhlam Amer Al Maskari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmal Hasan M\u003c/p\u003e\n \u003cp\u003eAbu Obaideh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmeera Al Shehhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmna Tamer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAndreas Henschel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKhalifa University, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnumol Surendhren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBalqees Al Hayyas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBushra\u003c/p\u003e\n \u003cp\u003eAbdulrahman Al Ghailani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaniyas Healthcare Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDean B Everett\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKhalifa University, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDoaa Hussain Saad Elmelegy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaniyas Healthcare Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnan Abdalkareem Nawafleh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaisal Al Ahbabi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaouzi Ben Tijani Zarka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarida Al Hosani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited Arab Emirates University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFatima Hadi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFouzia Jabeen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePureLab, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFrancis Amirtharaj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003cp\u003eClinical Microbiology and Immunology Laboratory, Research Laboratories, Khalifa University, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGamal\u003c/p\u003e\n \u003cp\u003eMohamed Hasan Ahmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGracelin Vedmani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMediclinic Hospital airport road branch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHala Imambaccus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJancy Varghese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJayalal Chellappan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJennifer\u003c/p\u003e\n \u003cp\u003eCalapan Dequito\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJherick Kagayutan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKanad Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ejherick.kagayutan@kanadhospital\u003c/p\u003e\n \u003cp\u003e.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJocelyn Lacanaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJoselita Ladline Rego\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKheir Mahmoud\u003c/p\u003e\n \u003cp\u003eAbou elkheir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu\u003c/p\u003e\n \u003cp\u003eDhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKhulood Khaled Al Blooshi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKiran Kumar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKristine Encelan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMadikay Senghore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKhalifa University, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMahra Al Hosani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaitha AlMansoori\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMariam Rashed\u003c/p\u003e\n \u003cp\u003eAl Saedi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Muwaiji Health center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarivic E. Astillero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKanad Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u0026nbsp;rg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMasitulah\u003c/p\u003e\n \u003cp\u003eNambalye\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Dhafra Family Medicine Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMauricio Paton Gasso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKhalifa University, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonet Abraham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMediclinic Hospital airport road branch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNael Sahhar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKanad Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePamela Fares Murad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePia Samson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKanad Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrameela Maniamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRagi George\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMadinat Khalifa Healthcare Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReham Jafer Al Hajjeh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRevathi\u003c/p\u003e\n \u003cp\u003eAngamuthu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Muwaiji Health center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRobert B. Custodio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMadinat Khalifa Healthcare Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRosula Janelle\u003c/p\u003e\n \u003cp\u003eMallillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Dhafra Family Medicine Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSadeq Abdel\u003c/p\u003e\n \u003cp\u003eRahman Shehadeh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSahar Almarzooqi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSajeed Abdulkader\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious Disease, PureLab, Sheikh Khalifa\u003c/p\u003e\n \u003cp\u003eMedical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSalwa Mohamed Ali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSalwa\u003c/p\u003e\n \u003cp\u003eMohamed Youssef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSamina Yousaf Yousaf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Dhafra Family Medicine Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSantosh Abraham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSara Abdi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbu Dhabi Public Health Center, Abu Dhabi, United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShaikha Jasim Al Zaabi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMadinat Khalifa Healthcare Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheena Kabeer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakhbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSiny Krishnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSheikh Shakbout Medical City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouby Pothen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Muwaiji Health center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStefan Weber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePureLab, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStephanie Kersi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSusan Abraham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Muwaiji Health center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSusmitha Vijayan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaniyas Healthcare Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTarteel Abdallah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Laboratory for Infectious\u003c/p\u003e\n \u003cp\u003eDisease, PureLab, Sheikh Khalifa Medical City, Abu Dhabi, UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFleming-Dutra, K. 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Med.\u003c/em\u003e \u003cstrong\u003e390\u003c/strong\u003e, 1009\u0026ndash;1021 (2024).\u003c/li\u003e\n\u003cli\u003eScott, J. \u003cem\u003eet al.\u003c/em\u003e Updated Evidence for Covid-19, RSV, and Influenza Vaccines for 2025\u0026ndash;2026. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cstrong\u003e0\u003c/strong\u003e,.\u003c/li\u003e\n\u003cli\u003eKrarup, A. \u003cem\u003eet al.\u003c/em\u003e A highly stable prefusion RSV F vaccine derived from structural analysis of the fusion mechanism. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 8143 (2015).\u003c/li\u003e\n\u003cli\u003eKramer, S. C., Pirikahu, S., Casalegno, J.-S. \u0026amp; Domenech de Cell\u0026egrave;s, M. Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control. \u003cem\u003eNat. 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EMBOSS: The European Molecular Biology Open Software Suite. \u003cem\u003eTrends Genet.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 276\u0026ndash;277 (2000).\u003c/li\u003e\n\u003cli\u003eCingolani, P. \u003cem\u003eet al.\u003c/em\u003e A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. \u003cem\u003eFly (Austin)\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 80\u0026ndash;92 (2012).\u003c/li\u003e\n\u003cli\u003ePuhr, R., Heinze, G., Nold, M., Lusa, L. \u0026amp; Geroldinger, A. Firth\u0026rsquo;s logistic regression with rare events: accurate effect estimates and predictions? \u003cem\u003eStat. Med.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2302\u0026ndash;2317 (2017).\u003c/li\u003e\n\u003cli\u003eThadani, N. 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S. \u003cem\u003eet al.\u003c/em\u003e Structure-Based Design of a Fusion Glycoprotein Vaccine for Respiratory Syncytial Virus. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e342\u003c/strong\u003e, 592\u0026ndash;598 (2013).\u003c/li\u003e\n\u003cli\u003eSun, Y. \u003cem\u003eet al.\u003c/em\u003e A potent broad-spectrum neutralizing antibody targeting a conserved region of the prefusion RSV F protein. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 10085 (2024).\u003c/li\u003e\n\u003cli\u003eNgwuta, J. O. \u003cem\u003eet al.\u003c/em\u003e Prefusion F\u0026ndash;specific antibodies determine the magnitude of RSV neutralizing activity in human sera. \u003cem\u003eSci. Transl. Med.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 309ra162-309ra162 (2015).\u003c/li\u003e\n\u003cli\u003eOraby, A. K. \u003cem\u003eet al.\u003c/em\u003e A single amino acid mutation alters multiple neutralization epitopes in the respiratory syncytial virus fusion glycoprotein. \u003cem\u003eNpj Viruses\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 33 (2025).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic and clinical characteristics of the study cohort.\u0026nbsp;\u003c/strong\u003eComparison of patient demographics and clinical features across three distinct cohorts. P-values are derived from Chi-squared or Fisher's exact tests for categorical variables and Kruskal-Wallis test for continuous variables. RSV subtype categories are not mutually exclusive because some patients were positive for both RSV-A and RSV-B.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute (N=458)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmitted (N=1278)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU (N=614)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.165 (19.635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.544 (9.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.005 (8.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.045 (3.415, 24.605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000 (0.090, 2.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.600 (0.080, 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOver 55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 - 55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 - 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 - 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e575 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e217 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnder 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e626 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e366 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNMiss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e243 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e578 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e211 (46.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e700 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e366 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRSV.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e501 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e352 (57.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRSV.B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e372 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e787 (61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e264 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9344370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9344370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Respiratory syncytial virus (RSV) remains a major cause of severe respiratory disease in young children, and the rollout of vaccines and monoclonal antibodies has increased the importance of genomic surveillance. Here, we combined epidemiological analysis of 2,350 laboratory confirmed RSV infections recorded in the United Arab Emirates between 2018 and 2023 with genomic surveillance of 312 RSV positive clinical isolates collected during the 2023 to 2024 season, integrating global phylogenetic contextualization and model based variant prioritization. Severe RSV disease in the UAE was concentrated in infants and young children, who accounted for most hospital and intensive care admissions. Phylogenetic analysis showed that RSV circulation in the UAE was shaped by repeated introductions of globally circulating RSV-A and RSV-B lineages, followed by local transmission and diversification. UAE RSV-B variants also showed elevated model predicted escape burden relative to year and substitution count matched public sequences, with the strongest signals arising from a subset of circulating variants rather than the most common recurrent substitutions. Together, these findings highlight the value of surveillance in the UAE for understanding RSV circulation in a globally connected setting and show how integrated epidemiological, genomic, and evolutionary analyses can prioritize variants for continued surveillance and experimental evaluation. ","manuscriptTitle":"Genomic surveillance in the UAE reveals the global origins and local diversification of RSV lineages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 10:25:49","doi":"10.21203/rs.3.rs-9344370/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"84a21bfb-ddb3-4227-b22c-caa34b8a7b20","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66212209,"name":"Biological sciences/Microbiology/Virology/Viral evolution"},{"id":66212210,"name":"Health sciences/Diseases/Infectious diseases"}],"tags":[],"updatedAt":"2026-04-21T04:15:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 10:25:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9344370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9344370","identity":"rs-9344370","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}