Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model | 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 Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model Sankalp Tiwari, C. P. Vyasarayani, Anindya Chatterjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-87296/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2021 Read the published version in Scientific Reports → Version 1 posted You are reading this latest preprint version Abstract People in many countries are now infected with COVID-19. By now, it is clear that the number of people infected is much more than the number of reported cases. To estimate the infected but undetected/unreported cases using a mathematical model, we can use a parameter called the probability of quarantining an infected individual. This parameter exists in the time-delayed SEIQR model (Scientific Reports, article number: 3505). Two limiting cases of a network of such models are used to estimate the undetected population. The first limit corresponds to the network collapsing onto a single node and is referred to as the mean-$\beta$ model. In the second case, the number of nodes in the network is infinite and results in a continuum model, treating the infectivity as statistically distributed. We use a shifted Pareto distribution to model the infectivity. This distribution has a long tail and incorporates the presence of super-spreaders that contribute to the disease progression. While both the models capture the {\em detected} numbers equally well, the predictions of {\em affected} numbers from the continuum model are more realistic. Results suggest that affected people outnumber detected people by one to two orders of magnitude in Spain, UK, Italy, and Germany. Epidemiology Applied Mathematics COVID 19 undetected population SEIQR time delay Pareto distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2021 Read the published version in Scientific Reports → 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-87296","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":3051002,"identity":"9a3e737c-6dd2-4e70-8167-2e0d24c0d3cc","order_by":0,"name":"Sankalp Tiwari","email":"","orcid":"","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sankalp","middleName":"","lastName":"Tiwari","suffix":""},{"id":3051003,"identity":"d6d2c072-3394-485a-bdac-cbf5b093f38a","order_by":1,"name":"C. P. 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By now, it is clear that the number of people infected is much more than the number of reported cases. To estimate the infected but undetected/unreported cases using a mathematical model, we can use a parameter called the probability of quarantining an infected individual. This parameter exists in the time-delayed SEIQR model (Scientific Reports,\u0026nbsp;article number: 3505). Two limiting cases of a network of such models are used to estimate the undetected population. The first limit corresponds to the network collapsing onto a single node and is referred to\u0026nbsp;as the mean-$\\beta$ model.\u0026nbsp;In the second case, the number of nodes in the network is infinite and results in a continuum model, treating the infectivity as statistically distributed.\u0026nbsp;We use a shifted Pareto distribution to model the infectivity. 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