From Data to Decisions: A Modular Platform for modelling and Simulation of Infectious Disease Diffusion in Networks

From Data to Decisions: A Modular Platform for modelling and Simulation of Infectious Disease Diffusion in Networks | 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 Research Article From Data to Decisions: A Modular Platform for modelling and Simulation of Infectious Disease Diffusion in Networks Francesco Branda, Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7253638/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 5 You are reading this latest preprint version Abstract Accurately modelling diffusion dynamics in complex networks is essential for improving medical outcomes, guiding pandemic preparedness, and optimizing resource allocation in public health. However, existing approaches often face a trade-off between predictive performance and model interpretability, limiting their utility for clinical decision-making and strategic planning. This study presents a modular computational methodology that integrates classical compartmental models with graph neural networks (GNNs) and explainable artificial intelligence (XAI) to simulate, analyse, and interpret the spread of contagion across heterogeneous network topologies. The approach captures both structural and temporal dimensions of diffusion processes, enabling granular insights into transmission pathways. Simulations are applied to critical public health scenarios, including the identification of super-spreaders and the assessment of targeted containment strategies. By combining mechanistic models with data-driven learning and explainability techniques, the methodology supports outcome forecasting, scenario comparison, and the interpretation of network-based risk factors. Results demonstrate the ability to predict diffusion trajectories with high accuracy while preserving transparency in decision-relevant variables. The approach is intended as a generalizable tool to support medical modelling and simulation with applications ranging from epidemic control to personalized risk assessment and cost-effective intervention planning. Network simulation Diffusion Modelling Graph Theory Explainable Artificial Intelligence Stochastic Processes Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 13 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 08 Oct, 2025 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-7253638","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538528603,"identity":"8ccd5c42-fcf8-4bf4-a60e-4d76d7d4ae64","order_by":0,"name":"Francesco Branda","email":"","orcid":"","institution":"Università Campus Bio-Medico","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Branda","suffix":""},{"id":538528604,"identity":"51e3ad27-0b79-4529-af02-43ce3e1eebe6","order_by":1,"name":"Annamaria Defilippo","email":"","orcid":"","institution":"Magna Graecia 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