{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:46:32Z","timestamp":1773801992187,"version":"3.50.1"},"reference-count":34,"publisher":"The Royal Society","issue":"236","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Although case numbers can describe the general dynamics of an epidemic, they have limited capacity to determine behavioural characteristics of disease transmission. Such insights are critical for enabling health authorities to better predict and respond to future epidemics. An optimal probabilistic linkage approach was applied to 18\u2009383 Australian Delta-variant SARS-CoV-2 genetic sequences and 112 551 confirmed Delta case reports. The resulting inferred transmission networks allowed quantification of spatial spread and age-group-specific behavioural characteristics of SARS-CoV-2 transmission in Australia. Most transmissions occurred within the same age groups, although individuals aged 25\u201339 years were often the greatest contributors to infections in those aged 70+. Spatial patterns revealed most transmissions (83%) occurred locally within 50 km, while this pattern significantly evolved in response to public health interventions. In every age group, over 43% of cases led to no onward transmission. Metropolitan regions with a high proportion of young individuals contributed substantially to infections in other regions. These results describe the impact of societal aspects underlying SARS-CoV-2 spread that can better inform government responses to future epidemics. They highlight the critical role of working-age adults in intergenerational transmission and quantify age-structured and spatially explicit parameters for future epidemic modelling and policy design.<\/jats:p>","DOI":"10.1098\/rsif.2025.0722","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:07:21Z","timestamp":1773792441000},"source":"Crossref","is-referenced-by-count":0,"title":["Characteristics of SARS-CoV-2 transmission in Australia"],"prefix":"10.1098","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9314-2283","authenticated-orcid":false,"given":"John M.","family":"Murray","sequence":"first","affiliation":[{"name":"UNSW Sydney 1 , , ,","place":["Sydney, New South Wales, Australia"],"department":["School of Mathematics and Statistics"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1710-3693","authenticated-orcid":false,"given":"Elma H.","family":"Akand","sequence":"additional","affiliation":[{"name":"UNSW Sydney 1 , , ,","place":["Sydney, New South Wales, Australia"],"department":["School of Mathematics and Statistics"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"175","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"2026031720071384700_R1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009146","article-title":"OpenABM-Covid19\u2014an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing","volume":"17","author":"Hinch","year":"2021","journal-title":"PLoS Comput. 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