Yunzhu Xiao , Wenjie Li , Yanyi Nie , Jiayi Song , Manrui Zhao , Zengping Zhang , Xiaoyang Liu , Yong Tang , Wei Wang
{"title":"Modeling two competing infectious diseases in a metropolitan contact network","authors":"Yunzhu Xiao , Wenjie Li , Yanyi Nie , Jiayi Song , Manrui Zhao , Zengping Zhang , Xiaoyang Liu , Yong Tang , Wei Wang","doi":"10.1016/j.chaos.2025.116282","DOIUrl":null,"url":null,"abstract":"<div><div>Infectious diseases rarely spread in isolation, and the competing spreading of multiple diseases widely exists. However, existing studies on the dynamics of competing infectious diseases typically rely on networked populations, lacking systematic research on specific metropolitan areas. This study proposes a competing infectious disease model to describe two competing infectious diseases spreading in metropolitan areas. An analytical framework of the theory is first developed to characterize this high-dimension system though expanded mean-field theory. To describe this spreading process more conveniently, we propose a dimension-reduction method to reduce the system complexity. Finally, we use an age-contact data-driven approach to simulate the spreading of two competing infectious diseases in metropolitan cities like New York. The results validate our dimension-reduction method’s reliability, allowing us to describe the high-dimension system through a one-dimensional equation. We observed three main scenarios of competing disease spreading, i.e., neither disease can break out, one disease dominates, while the other is suppressed, and the two diseases coexist throughout the spreading. Moreover, our simulations show that the workplace favours specific diseases with stronger spreading capabilities than other settings.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"196 ","pages":"Article 116282"},"PeriodicalIF":5.3000,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960077925002954","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Infectious diseases rarely spread in isolation, and the competing spreading of multiple diseases widely exists. However, existing studies on the dynamics of competing infectious diseases typically rely on networked populations, lacking systematic research on specific metropolitan areas. This study proposes a competing infectious disease model to describe two competing infectious diseases spreading in metropolitan areas. An analytical framework of the theory is first developed to characterize this high-dimension system though expanded mean-field theory. To describe this spreading process more conveniently, we propose a dimension-reduction method to reduce the system complexity. Finally, we use an age-contact data-driven approach to simulate the spreading of two competing infectious diseases in metropolitan cities like New York. The results validate our dimension-reduction method’s reliability, allowing us to describe the high-dimension system through a one-dimensional equation. We observed three main scenarios of competing disease spreading, i.e., neither disease can break out, one disease dominates, while the other is suppressed, and the two diseases coexist throughout the spreading. Moreover, our simulations show that the workplace favours specific diseases with stronger spreading capabilities than other settings.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.