{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:37:15Z","timestamp":1779403035014,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686615","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,21]]},"abstract":"<jats:p>This study presents a hybrid modelling framework that integrates classical statistical methods (ARIMA) with deep learning architectures to enhance longitudinal forecasting of hospital admissions. We conducted a preliminary, hospital-based evaluation of the early impact of nirsevimab (Beyfortus\u00ae), a monoclonal antibody against respiratory syncytial virus (RSV), on infant hospitalisations at the H\u00f4pitaux Universitaires de Gen\u00e8ve (HUG), Switzerland. Weekly RSV-positive admissions (April 2021\u2013October 2025) were analysed using ARIMA\u2013Fourier and hybrid deep learning models trained on pre-intervention data (January 2022\u2013October 2024). Estimated reductions in RSV-related hospitalisations were 44% for infants under 12 weeks (ARIMA 44.3%, 95% CI [21.7\u2013143]) and 39% for those under 12 months (ARIMA 39.5%, 95% CI [35\u2013280]). Hybrid deep learning models yielded consistent reductions (35\u201348%), confirming predominantly linear RSV dynamics and reinforcing confidence in the observed post-nirsevimab decline. These preliminary findings demonstrate the potential of hybrid statistical\u2013machine learning frameworks for early, interpretable, and robust evaluation of vaccine impact using real-world hospital data.<\/jats:p>","DOI":"10.3233\/shti260138","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:56:22Z","timestamp":1779400582000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrating ARIMA and Deep Learning Models for Counterfactual Evaluation of Nirsevimab\u2019s Early Impact on RSV Infant Admissions"],"prefix":"10.3233","author":[{"given":"Gilles","family":"Cohen","sequence":"first","affiliation":[{"name":"Finance Division, University Hospital of Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martine","family":"Fallot","sequence":"additional","affiliation":[{"name":"Finance Division, University Hospital of Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Opening the Personal Gate between Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:56:23Z","timestamp":1779400583000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"ISBN":["9781643686615"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260138","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}