{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:26:30Z","timestamp":1761171990327,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lili Rong","award":["72271041","72434001"],"award-info":[{"award-number":["72271041","72434001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Population mobility links cities, propelling the spatiotemporal spread of urban pandemics and adding complexity to disease dynamics. It also closely shapes, and is shaped by, the selection and intensity of intervention measures. Revealing the multistage spatial-temporal dynamics of cross-regional epidemic continuity under this interaction is often overlooked but critically important. This study innovatively applies a self-organizing map (SOM) neural network to classify cities into six distinct types based on population mobility characteristics: high-inflow core (HIC), low-inflow core (LIC), low-inflow sub-core (LISC), high-outflow semi-peripheral (HOSP), equilibrious semi-peripheral (ESP), and low-outflow peripheral (LOP). Building on this, we propose a novel SEIR-AHQ theoretical framework and construct an epidemiological model using network-coupled ordinary differential equations (ODEs). This model captures the dynamic interplay between inter-city population mobility and intervention measures, and quantifies how heterogeneous city types shape the evolution of epidemic transmission across the coupled mobility network. The results show that: (1) Cities with stronger population mobility face significantly higher infection risks and longer epidemic durations, characterized by \u201chigher peaks and longer tails\u201d in infection curves. HIC cities experience the greatest challenges, and LOP cities experience the least. (2) Both higher transmission rates and delayed intervention timings lead to exponential growth in infections, with nonlinear effects amplifying small changes disproportionately. (3) Intervention efficacy follows a \u201cdiminishing marginal returns\u201d pattern, where the incremental benefits of increasing intervention intensity gradually decrease. This study offers a novel perspective on managing interregional epidemics, providing actionable insights for crafting tailored and effective epidemic response strategies.<\/jats:p>","DOI":"10.3390\/systems13100923","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T15:20:12Z","timestamp":1760973612000},"page":"923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unraveling the Interaction Between Intercity Mobility and Interventions: Insights into Cross-Regional Pandemic Spread"],"prefix":"10.3390","volume":"13","author":[{"given":"Yue","family":"Feng","sequence":"first","affiliation":[{"name":"School of Economics and Management, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Cong","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoyang","family":"Bu","sequence":"additional","affiliation":[{"name":"School of Management, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eabb6936","DOI":"10.1126\/science.abb6936","article-title":"Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing","volume":"368","author":"Ferretti","year":"2020","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1287\/mnsc.2021.4240","article-title":"The social divide of social distancing: Shelter-in-place behavior in santiago during the COVID-19 pandemic","volume":"68","author":"Carranza","year":"2022","journal-title":"Manag. 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