Transmission dynamic of respiratory multi-pathogen co-infection in a Chinese megacity: A modelling study based on Wuhan Big Data Center

Transmission dynamic of respiratory multi-pathogen co-infection in a Chinese megacity: A modelling study based on Wuhan Big Data Center | 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 Transmission dynamic of respiratory multi-pathogen co-infection in a Chinese megacity: A modelling study based on Wuhan Big Data Center Tianmu Chen, Jiahui Li, Banghua Chen, Yunkang Zhao, Yuhao Lin, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9168824/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract To investigate the transmission dynamics of respiratory multi-pathogen co-infection, this study developed a dual-pathogen co-infection transmission dynamics model comprising 10 compartments, incorporating an exponentially decaying time-varying effective transmission rate. The model was systematically calibrated and validated using epidemiological surveillance data from a major Chinese metropolis—Wuhan—covering approximately 2.72 million cases between January 1, 2023 and January 1, 2025. The analysis focused on the 13 most common pathogen combinations, which accounted for the top 1% of infection cases. Results revealed three distinct winter-centered epidemic peaks in Wuhan over the two-year period. Co-infection cases were most frequently observed as virus–bacteria and virus–other pathogen combinations. Analysis of the major co-infection combinations showed that the mean basic reproduction number of the system ranged between 1.19 and 1.88, while the median ranged from 1.08 to 1.35. Transmission peaks in each combination were primarily driven by the most transmissible pathogen within the pair. Transmission route analysis further indicated that when individuals were infected with multiple pathogens, sequential secondary infection was the dominant pattern, whereas direct simultaneous co-infection occurred at a relatively low proportion. This study suggests that respiratory pathogens continue to exhibit sustained transmission potential. The findings provide an important evidence based reference for designing risk oriented, precision prevention and control strategies in the future. Health sciences/Diseases/Respiratory tract diseases Physical sciences/Mathematics and computing/Applied mathematics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.docx Supplementary of Transmission dynamic of respiratory multi-pathogen co-infection in a Chinese megacity: A modelling study based on Wuhan Big Data Center Cite Share Download PDF Status: Under Review 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-9168824","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617649104,"identity":"260cd4a5-6589-4d75-8f99-ef4c8e8d0795","order_by":0,"name":"Tianmu 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