{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T18:10:13Z","timestamp":1779214213328,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014279","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000}}],"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Rospotrebnadzor (The Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing)","award":["141-02-2023-208"],"award-info":[{"award-number":["141-02-2023-208"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Human mobility is a key driver of early epidemic spread, and restricting travel remains one of the principal non-pharmaceutical interventions. To better understand how infections propagate through real-world mobility networks, it is essential to disaggregate their components and characterize the functional relationships between mobility flows and epidemic metrics. Here we introduce transCovasim, an agent-based extension of Covasim that explicitly couples parallel city simulations via inter-city traveler exchange while preserving the initial social network structure within each population, enabling controlled experiments on mobility and disease dynamics. Using transCovasim, we analyze a two-city system with equal or unequal populations and a hub-and-satellite commuting network parameterized to a Moscow-like setting. In paired identical cities, the mean lag between epidemic peaks scales approximately linearly with the logarithm of inter-city traffic, with steeper delays at lower transmissibility; epidemic variability declines as flows increase. With unequal city sizes, mobility primarily redistributes infections between cities; first-order Sobol\u2019 indices show that peak magnitude is largely insensitive to city\u2019s flows of different directions when sizes are comparable, while sensitivity for a smaller city shifts toward flow from a bigger city as the asymmetry increases. In the hub-and-satellite network, reducing commuting flows before the peak significantly lowers peak incidence, and cumulative infections can still be reduced when restrictions are introduced after the peak; early 100-fold cuts outperform 10-fold cuts, but produce similar results when introduced into the late exponential phase. Finally, dynamic time warping applied to surveillance curves identifies the outbreak\u2019s origin: under Moscow-like flows, accuracy reaches 78% by day 50 with 10% daily testing, and approaches 100% at lower connectivity. These results clarify how specific mobility patterns shape epidemic timing and burden and provide actionable guidance for mobility-targeted non-pharmaceutical interventions and early source attribution.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014279","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:43:07Z","timestamp":1778607787000},"page":"e1014279","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human mobility and outbreak origins in epidemic spread: Insights from agent-based modeling"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2839-6189","authenticated-orcid":true,"given":"Konstantin A.","family":"Klochkov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan E.","family":"Kozlov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena N.","family":"Ilina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander I.","family":"Manolov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"issue":"5","key":"pcbi.1014279.ref001","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.3201\/eid2605.200146","article-title":"Risk for transportation of coronavirus disease from Wuhan to other cities in China","volume":"26","author":"Z Du","year":"2020","journal-title":"Emerging Infectious Diseases"},{"issue":"4","key":"pcbi.1014279.ref002","doi-asserted-by":"crossref","first-page":"2000057","DOI":"10.2807\/1560-7917.ES.2020.25.4.2000057","article-title":"Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, 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