{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T18:03:15Z","timestamp":1784570595614,"version":"3.55.0"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2025YFA1016500"],"award-info":[{"award-number":["2025YFA1016500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2025YFA1016500"],"award-info":[{"award-number":["2025YFA1016500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project","award":["2025ZD01900400"],"award-info":[{"award-number":["2025ZD01900400"]}]},{"name":"Shanghai Institute for Mathematics and Interdisciplinary Sciences","award":["SIMIS, SIMIS-ID-2025-NW"],"award-info":[{"award-number":["SIMIS, SIMIS-ID-2025-NW"]}]},{"name":"Shanghai Institute for Mathematics and Interdisciplinary Sciences","award":["SIMIS, SIMIS-ID-2025-NW"],"award-info":[{"award-number":["SIMIS, SIMIS-ID-2025-NW"]}]}],"content-domain":{"domain":["www.plosmedicine.org"],"crossmark-restriction":false},"short-container-title":["PLoS Med"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Understanding how respiratory infectious diseases spread across cities of different socioeconomic tiers is crucial for regionally targeted interventions. However, most spatial prediction frameworks neglect the combined influence of urban hierarchy and human mobility in shaping transmission risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>\n                      We integrated large-scale intercity mobility data into an agent-based branching process model to simulate the spatial diffusion of respiratory pathogens across mainland China. Three COVID-19 outbreaks were used for validation: the Omicron outbreak in Shanghai, the Delta outbreak in Nanjing, and a multi-provincial Delta outbreak in northwestern China. We applied the framework to model the spread of SARS-CoV-2 (Omicron variant) and Influenza A to quantify tier-specific transmission risks. Tiers denote a hierarchical classification of Chinese cities based on concentration of commercial resources, transportation hub centrality, etc., ranging from super-tier metropolises to lower-tier cities. Predicted first arrival times showed strong agreement with observed data (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.68 and 0.76), and mobility-based predictions more accurately identified outbreak origins than distance-based approaches. Markedly different tier-dependent diffusion patterns were observed across pathogens. Influenza A exhibited stable and stratified diffusion, with transmission confined mainly within the same or adjacent urban tiers and limited cross-tier seeding. In contrast, SARS-CoV-2 (Omicron) initially concentrated in super-tier and tier-1 cities but rapidly spread to lower-tier cities, producing a pronounced hierarchical pattern of spread that quickly diminished tier-level differences in transmission risk. Across pathogens, higher-tier cities consistently faced greater early importation risk; however, this disparity persisted for Influenza A but was rapidly attenuated for Omicron due to its high transmissibility and fast spatial expansion. A key limitation is that the model was validated against city-level first arrival times rather than full epidemic dynamics,\u00a0and was parameterised using mobility data from\u00a0China\u2019s \u201cdynamic zero-COVID\u201d period, which may limit direct quantitative generalisability to other settings.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Spatial transmission risk reflects an interaction between pathogen-specific transmissibility and the hierarchical organisation of China\u2019s urban mobility system. These findings indicate that surveillance and response strategies effective for less transmissible pathogens may be insufficient for highly transmissible variants. Mobility-informed, tier-specific risk assessment may help inform early warning and support more adaptive public health responses. Because the framework relies on routinely available mobility data and minimal pathogen-specific inputs, it may provide a scalable approach for epidemic preparedness, including potential future emerging respiratory threats (Disease X).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pmed.1005172","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:23:29Z","timestamp":1784568209000},"page":"e1005172","update-policy":"https:\/\/doi.org\/10.1371\/journal.pmed.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing spatial transmission risk of respiratory infectious diseases across cities of different socioeconomic tiers in China: A modelling 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