{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:05:52Z","timestamp":1755223552536,"version":"3.43.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2022A1515011345"],"award-info":[{"award-number":["2022A1515011345"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Education Science Planning Project of Guangdong Province","award":["2023GXJK486"],"award-info":[{"award-number":["2023GXJK486"]}]},{"name":"Network and Cloud Computing Engineering Centre of Meizhou City","award":["2023GC03"],"award-info":[{"award-number":["2023GC03"]}]},{"name":"University Key Field Project of Guangdong Province","award":["2024ZDZX1024"],"award-info":[{"award-number":["2024ZDZX1024"]}]},{"name":"Teaching Quality and Innovation Engineering Project of the Jiaying University","award":["ZLGC2024503"],"award-info":[{"award-number":["ZLGC2024503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p> Epidemic modeling for prompt identification of risky areas is vital for preventing the spread of respiratory infectious diseases such as COVID-19. Since everyone is a mobile entity and society is a dynamic system with variable network topology, studies on epidemic modeling should be done within mobile scenarios. Some research works discussed epidemic modeling related to mobility patterns, but most of them focused on specific regions or relied on certain data sets with transient and time-varying characteristics, which severely limited their applications. To solve this problem, based on the consensus that the spread of the epidemic in a certain region is positively correlated to the population density in that region, this paper discussed the stationary node distribution of a region. By establishing two individual entity mobility models, this paper adopts the methods of stochastic process theory and network control theory to rigorously analyze the stationary characteristics of population density and node distribution in a social dynamic system. The probability density function of the stationary node distribution is calculated and expressed in the form of an elliptical integral. Moreover, two groups of simulation experiments were designed to verify the correctness of theoretical analysis. The results of theoretical analysis and simulation experiments show that the random chord mobility model and random walk mobility model exhibit completely opposite stationary node distribution characteristics. This paper contributes to early identifying high-risk regions with high population density and high spreading rate of the epidemic, so as to provide information support for early detection and prevention of the epidemic. <\/jats:p>","DOI":"10.1142\/s1793962325500503","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T10:07:26Z","timestamp":1749722846000},"source":"Crossref","is-referenced-by-count":0,"title":["Analysis of two mobility models for epidemic modeling"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5195-1257","authenticated-orcid":false,"given":"Na","family":"Qin","sequence":"first","affiliation":[{"name":"Department of Software Engineering, College of Computer Science, Jiaying University, Meizhou, P. R. 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