{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:52:56Z","timestamp":1791334376217,"version":"4.3.1"},"reference-count":70,"publisher":"The Royal Society","issue":"243","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Onside company"},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-24-1-0039"],"award-info":[{"award-number":["FA9550-24-1-0039"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"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>Human mobility plays a crucial role in the spread of human diseases but is rarely quantified in plant disease epidemics. To address this gap, we integrate a unique, high-resolution network of human movements in New Zealand with a metapopulation model to mechanistically simulate pathogen transmission. We calibrate the model on the nationwide 2010 kiwifruit vine disease (Psa-V) outbreak and show that it reproduces the observed spatio-temporal spread, confirming that the human mobility network is a strong foundation for modelling human-mediated transmission dynamics. By analysing spatial infection trends, we find that most dispersal occurs locally, as often illustrated in the plant-outbreak literature. However, sporadic long-range connections are necessary to model a nationwide outbreak. Using the model as an in silico laboratory, we demonstrate that the severity of human-mediated pathogen transmission is highly sensitive to the timing and location of initial importation. We observe a potential causal link between seasonal labour patterns and epidemic risk in high-traffic seasons. This study showcases a novel data-driven framework for modelling the spatio-temporal spread of agricultural pathogens when human-mediated dispersal is epidemiologically relevant, underscoring the importance of leveraging human mobility networks for building better biosecurity systems.<\/jats:p>","DOI":"10.1098\/rsif.2026.0213","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:22:30Z","timestamp":1791332550000},"source":"Crossref","is-referenced-by-count":0,"title":["Modelling plant disease spread via high-resolution human mobility networks"],"prefix":"10.1098","volume":"23","author":[{"given":"Varun K.","family":"Rao","sequence":"first","affiliation":[{"name":"Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University 1 , Bloomington, IN, USA"},{"name":"Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University 2 , Bloomington, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Higgs","sequence":"additional","affiliation":[{"name":"Onside 3 , Christchurch, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hautahi","family":"Kingi","sequence":"additional","affiliation":[{"name":"Onside 3 , Christchurch, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-1287","authenticated-orcid":false,"given":"Filippo","family":"Radicchi","sequence":"additional","affiliation":[{"name":"Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University 1 , Bloomington, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-4730","authenticated-orcid":false,"given":"Santo","family":"Fortunato","sequence":"additional","affiliation":[{"name":"Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University 1 , Bloomington, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6393-1943","authenticated-orcid":false,"given":"Maria","family":"Litvinova","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University 2 , Bloomington, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"175","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"2026100620222399900_RSIF20260213C1","doi-asserted-by":"crossref","first-page":"R574","DOI":"10.1016\/j.cub.2023.03.075","article-title":"Plant disease risk is modified by multiple global change drivers","volume":"33","author":"Laine","year":"2023","journal-title":"Curr. 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