Structural Characterization of Horizontal Visibility Network Based on Time Series of Emergency Disease Visits

Structural Characterization of Horizontal Visibility Network Based on Time Series of Emergency Disease Visits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Structural Characterization of Horizontal Visibility Network Based on Time Series of Emergency Disease Visits Linyuan Zhang, Man Zhou, Yuqi Liu, Kezhao Xiong, Li Zhang, Chao Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4436890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The conversion of time series into visualized networks is one of the most important tools for comprehending data patterns and trends. Here, we initially translated hospital emergency data into complex networks through horizontal visualization techniques. Subsequently, we carefully analyzed the topologies of the network structures corresponding to each category of disease by applying the structural statistics approach of network science. Our investigation unveiled that networks constructed by this method exhibit larger clustering coefficients and shorter average shortest paths of the networks, leading to a stronger small-world effect. Moreover, we observed that the average degree of the networks is notably higher for the respiratory diseases, which may be caused by the heightened contagious diseases in the respiratory diseases. Further, by calculating the maximum eigenvalue of the network Laplace matrix, we found that the maximum eigenvalue of the respiratory diseases are generally higher than other categories of diseases. This provides a crucial analytical tool for the proactive prevention of certain specific diseases and more effective response strategies during emergency triage protocols. Physical sciences/Physics/Statistical physics thermodynamics and nonlinear dynamics/Complex networks Health sciences/Diseases/Respiratory tract diseases Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4436890","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309530771,"identity":"104ae2af-2e14-496e-b2cf-53596698086d","order_by":0,"name":"Linyuan Zhang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":309530772,"identity":"fd951b19-da67-410f-8a47-9d3a92d83a07","order_by":1,"name":"Man Zhou","email":"","orcid":"","institution":"Xi'an University of Science and 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