{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:39:49Z","timestamp":1792543189283,"version":"4.5.0"},"reference-count":70,"publisher":"The Royal Society","issue":"243","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Faculty Startup Fund from Seoul National University"},{"name":"Global-LAMP Program of the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education","award":["RS-2023-00301976"],"award-info":[{"award-number":["RS-2023-00301976"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Infectious disease outbreaks typically exhibit a large-scale spatial synchrony, reflecting coupling through shared environmental forcing and human mobility. Exceptions to this theoretical expectation have been reported, but the conditions under which synchrony breaks down remain poorly understood. Republic of Korea's 2024\u20132025 pertussis outbreak provides a striking example, during which epidemic trajectories diverged substantially throughout the country: notably, the decline in spatial synchrony with distance was more pronounced than that reported in many previous outbreaks. Integrating high-resolution surveillance data from 252 municipalities and a Bayesian transmission model, we show that heterogeneity in introduction timing and differences in local transmission levels, which may reflect differences in either contact patterns or susceptibility, can drive such fine-scale asynchrony even when all locations share identical seasonal forcing for transmission rates. In particular, regional variation in baseline transmission levels can drive differential levels of susceptible depletion, thus shaping the potential for multiple epidemic waves. This analysis offers a unique perspective on spatio-temporal epidemic dynamics that depart from classical epidemic theory.<\/jats:p>","DOI":"10.1098\/rsif.2026.0205","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:18:41Z","timestamp":1792541921000},"source":"Crossref","is-referenced-by-count":0,"title":["Delayed introduction, transmission variation and susceptible dynamics explain spatial asynchrony during Republic of Korea's large pertussis outbreak"],"prefix":"10.1098","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2202-3361","authenticated-orcid":false,"given":"Sang Woo","family":"Park","sequence":"first","affiliation":[{"name":"School of Biological Sciences, Seoul National University 1 , Seoul, Republic of Korea"},{"name":"Institute for Data Innovation in Science, Seoul National University 2 , Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"175","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"key":"2026102020183739100_bib1","doi-asserted-by":"crossref","first-page":"1004591","DOI":"10.1371\/journal.ppat.1004591","article-title":"Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States","volume":"11","author":"Pitzer","year":"2015","journal-title":"PLoS Pathog."},{"key":"2026102020183739100_bib2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1126\/science.aat6030","article-title":"Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities","volume":"362","author":"Dalziel","year":"2018","journal-title":"Science"},{"key":"2026102020183739100_bib3","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1073\/pnas.1721159115","article-title":"The seasonality of nonpolio enteroviruses in the United States: patterns and drivers","volume":"115","author":"Pons-Salort","year":"2018","journal-title":"Proc. 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