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Higher susceptibility to RSV infections in young children, interacting with age-varying contact patterns and risk of disease progression, could drive the age-related variations in disease burden, though the extent of the susceptibility differences, and their influence on transmission dynamics and immunisation impact remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an age-structured deterministic transmission model that integrated virological surveillance data, hospitalisations, and infection rates from the UK. We estimated age-dependent susceptibility coefficients for the 0\u2013\u2009&lt;\u20095\u00a0years, 5\u201359\u00a0year and\u2009\u2265\u200960\u00a0years by integrating a profile likelihood approach with Markov Chain Monte Carlo (MCMC)-based calibration. Models were fitted to weekly RSV-positive cases in Scotland and infection rate estimates for the UK population. Age-specific infection-hospitalisation ratios (IHRs) were derived by combining modelled infections with RSV-associated hospitalisations. We evaluated multiple paediatric immunisation scenarios by estimating infections and hospitalisations averted, and the number of individuals needed to immunise (NNI) to prevent one RSV hospitalisation at varying coverage and efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We estimated a susceptibility coefficient of 0.38 (95% CI 0.36\u20130.38) for 5\u201359\u00a0years, and 0.38 (0.20\u20130.40) for\u2009\u2265\u200960\u00a0years, relative to those under 5\u00a0years. The overall annual infection rate was 51.8%, with peaked in children aged 12\u201323\u00a0months (71.1%) and 0\u20132\u00a0months (63.7%) using the best-fitting model, showing a substantial shift in the age distribution compared to the base model. IHRs showed a U-shaped distribution, with the highest rates in infants aged 0\u20132\u00a0months and adults aged 75 and above. This model also projected a greater impact from immunisation programmes compared to the base model. For instance, an infant immunisation programme with 80% coverage and 80% efficacy against hospitalisation was projected to prevent 15.2% of hospitalisations, compared to 8.9% in the base model. Broader programmes, such as targeting 0\u20134-year-olds, resulted in larger reductions in hospitalisations (27.1%), while NNI increased as the programme expanded.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The increased RSV susceptibility in children under 5\u00a0years drives higher baseline transmission rates and disease burden in this subgroup, thereby influencing immunisation programme impact and efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12916-026-04776-1","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:04:59Z","timestamp":1773191099000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The influence of age-dependent susceptibility on RSV transmission dynamics and immunisation population-level impact"],"prefix":"10.1186","volume":"24","author":[{"given":"Chenkai","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Osei-Yeboah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harish","family":"Nair","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"4776_CR1","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1016\/S0140-6736(22)00478-0","volume":"399","author":"Y Li","year":"2022","unstructured":"Li Y, Wang X, Blau DM, Caballero MT, Feikin DR, Gill CJ, et al. 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X.L reports honoraria from GSK for an educational symposium. Y.L reports grants from National Natural Science Foundation of China, MSD, GSK and WHO; Personal fees from MSD, Pfizer and WHO; Honoraria from MSD and Pfizer; and travel grants from Sanofi and Pfizer, outside the submitted work. X.W reports grants from National Natural Science Foundation of China, MSD and GlaxoSmithKline; Personal fees from Pfizer; and honoraria from MSD, outside the submitted work. H.N reports grants from the National Institute for Health Research, the Innovative Medicines Initiative, MSD, AstraZeneca, Pfizer and WHO; Personal fees from AstraZeneca, Pfizer, GSK and Sanofi; and honoraria from Pfizer, outside the submitted work. All other authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"254"}}