{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:27:59Z","timestamp":1785421679557,"version":"3.56.0"},"reference-count":35,"publisher":"National Academy of Sciences","issue":"21","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100025294","name":"Research Ireland","doi-asserted-by":"publisher","award":["SFI\/12\/RC\/2289 P2"],"award-info":[{"award-number":["SFI\/12\/RC\/2289 P2"]}],"id":[{"id":"10.13039\/501100025294","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,5,26]]},"abstract":"<jats:p>\n                    Seasonal influenza epidemics exhibit complex transmission dynamics influenced by time-varying extrinsic factors such as social behavior and seasonal effects. Estimating changes in transmission rates is critical to enable accurate forecasting of the epidemic curve. This study presents a framework for detecting changepoints in the transmission parameter (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:msub>\n                          <mml:mi>\u03b2<\/mml:mi>\n                          <mml:mi mathvariant=\"normal\">t<\/mml:mi>\n                        <\/mml:msub>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ), applied as a piecewise constant function within a deterministic compartmental model. Using hospitalized case data from four recent influenza A seasons in Ireland (2019\/2020 and 2022\u20132025), we applied iterated filtering and kernel density estimation to identify season-specific and cross-seasonal changepoint structures. The algorithm integrates stochastic search, local perturbation, and resampling to infer the most plausible changepoint configurations. Results reveal consistent changepoint patterns across seasons, particularly during periods of increased social mixing, such as the December holiday period. A universal changepoint model was also developed, enabling medium-term forecasting and scenario planning. This approach offers a robust method for capturing abrupt shifts in transmission and may be applicable to other dynamical systems.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2533861123","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:04:32Z","timestamp":1779206672000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":0,"title":["Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6606-4611","authenticated-orcid":false,"given":"Ajay N.","family":"Oza","sequence":"first","affiliation":[{"name":"Respiratory Virus Unit, Health Service Executive-Health Protection Surveillance Centre","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1853-5663","authenticated-orcid":false,"given":"Katie M.","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"Modelling and Biostatistics Unit, Health Service Executive-Health Protection Surveillance Center","place":["Dublin, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3410-2817","authenticated-orcid":false,"given":"James P.","family":"Gleeson","sequence":"additional","affiliation":[{"name":"Modelling and Biostatistics Unit, Health Service Executive-Health Protection Surveillance Center","place":["Dublin, Ireland"]},{"id":[{"id":"https:\/\/ror.org\/00a0n9e72","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Mathematics and Statistics, Mathematics Applications Consortium for Science and Industry, University of Limerick","place":["Limerick, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1186\/1741-7015-7-45"},{"key":"e_1_3_4_2_2","unstructured":"ECDC Surveillance for seasonal influenza in Europe\u2013ECDC Technical Report. (2017). https:\/\/www.ECDCecdc.europa.eu\/en\/publications-data\/surveillance-seasonal-influenza-europe."},{"key":"e_1_3_4_3_2","unstructured":"HSE-HPSC Influenza surveillance in Ireland: Annual report. (2024). https:\/\/www.hpsc.ie\/a-z\/respiratory\/influenza\/surveillance\/. [cited 2025 15\/102\/025]."},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2021.0896"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-98975-w"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxs052"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011575"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-14979-0"},{"key":"e_1_3_4_9_2","article-title":"Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates","volume":"380","author":"Gleeson J. P.","year":"2022","unstructured":"J. P. 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