A simulation is worth a thousand worlds - Importance sampling Monte Carlo applied to COVID-19 contingency

A simulation is worth a thousand worlds - Importance sampling Monte Carlo applied to COVID-19 contingency | 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 A simulation is worth a thousand worlds - Importance sampling Monte Carlo applied to COVID-19 contingency Andrea Rapuzzi, Tomaso Vairo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-109108/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 COVID-19 outbreak has become a global pandemic that affected more than 200 countries worldwide. Predicting the behavior of this outbreak has a crucial role in organizing preventive and protective actions, and in improving the decision making process. The aim of predicting the number of people who contract the virus has so far been pursued with regression models (exponential, logistics, ...), but regressions can integrate the variable context only a posteriori. The regression models are all dependent on their own history, thus, they can not display anything which did not happen before. The pandemic infection of COVID-19 presents a transmission behavior that is widely changing over time. This is due to the growth of the efficiency in the detection of infected, for the changes in social distancing measures and for the widespread use of individual protection devices. The approach presented in this paper, starting from the definition of simplified risk assessment framework, aims at designing a probabilistic model for the virus transmission and detection, keeping into account this context changes, binding the correct set of variables to them, and at inferring the distribution for the underlying stochastic variables. This is a key to unlock innovative and valuable insights from the current events. The model has been built in Gen, a probabilistic programming system, built at MIT and embedded in Julia. Artificial Intelligence and Machine Learning Julia Language Predictive model Inference Gen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. 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-109108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":4831208,"identity":"7a038dfc-1386-4f58-bd40-0cfea2e2868c","order_by":0,"name":"Andrea Rapuzzi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHACA2S2DZBgbAASEkRrSYNrwacHWQvDYTgLpxb+2c3bPnyoYcjnn91j/PFHwXl5Bv7Dza8LGCzqcGmRuHOseOaMYwyWM+6cMTCQMLht2CCR2GY9A5/DbuQYM/OwAV13I8cgwcDgNuP+G4xtxjx4tMiDtPz5x2AAZBgcSDA4Z9/AfxC/FgOQFsY2BgMgw7DhgMGBxAaGxObH+LQY3kgrZuztkzAwBHqKscEgORnkF2YeAwnJBhxa5G4kb2b48c3GQO528+aPP/7Y2TbwH3/8maeijh+n9yFAAiUe2CRQIwuPLhhg/kCMhlEwCkbBKBgxAACeJ07npw3itAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6653-2857","institution":"A-SIGN","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Rapuzzi","suffix":""},{"id":4831209,"identity":"b22487e5-18ce-43ad-85cd-94bd85e23241","order_by":1,"name":"Tomaso Vairo","email":"","orcid":"","institution":"Università degli Studi di Genova","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tomaso","middleName":"","lastName":"Vairo","suffix":""}],"badges":[],"createdAt":"2020-11-16 15:28:39","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-109108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-109108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3619179,"identity":"bb552b4c-d731-473a-afc5-c353c0c8324b","added_by":"auto","created_at":"2020-11-16 23:33:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57172,"visible":true,"origin":"","legend":"Bow-Tie model for COVID-19 transmission","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/4b57b7785c868acff4e7d2f4.png"},{"id":3619180,"identity":"f3ad5637-c7bd-4131-bf8e-4f14605a2822","added_by":"auto","created_at":"2020-11-16 23:33:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43135,"visible":true,"origin":"","legend":"FT for COVID-19 transmission","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/124371d9685ea529be0d49a0.png"},{"id":3619181,"identity":"3be36fb6-2805-4032-b30d-3735abcd8a8d","added_by":"auto","created_at":"2020-11-16 23:33:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36768,"visible":true,"origin":"","legend":"ET for COVID-19 transmission","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/0a84817f5bd273415fd6be0c.png"},{"id":3619182,"identity":"a62124a2-71fc-4eaf-be70-ed3416809280","added_by":"auto","created_at":"2020-11-16 23:33:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155563,"visible":true,"origin":"","legend":"Unweighted approach","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/72b38841777f5af6f5c935db.png"},{"id":3619183,"identity":"94e14b06-8b2d-4ada-8f00-1a41ea87c9c5","added_by":"auto","created_at":"2020-11-16 23:33:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155745,"visible":true,"origin":"","legend":"Weighted windows approach","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/c2357ed536f613e9149df21a.png"},{"id":3619184,"identity":"e031b980-68c9-40ad-b769-c06df2f52f80","added_by":"auto","created_at":"2020-11-16 23:33:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":306303,"visible":true,"origin":"","legend":"Generated data","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/83b3cd4728ed527e464c0e7e.png"},{"id":3619185,"identity":"2180b1cd-31c0-42af-8237-29f12d457910","added_by":"auto","created_at":"2020-11-16 23:33:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":573608,"visible":true,"origin":"","legend":"Generated data cloud","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1/2867604a62a12592a27ffcf6.png"},{"id":13556290,"identity":"edb305b1-f3d1-4ed4-b2b1-a94e017602f4","added_by":"auto","created_at":"2021-09-17 02:48:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1147515,"visible":true,"origin":"","legend":"","description":"","filename":"RapuzziVairoCOVID.pdf","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1_covered.pdf"},{"id":3619186,"identity":"1ba5a116-df1c-42cd-8f1b-07c06bffffcd","added_by":"auto","created_at":"2020-11-16 23:33:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":846001,"visible":true,"origin":"","legend":"","description":"","filename":"RapuzziVairoCOVID.pdf","url":"https://assets-eu.researchsquare.com/files/rs-109108/v1_stamped.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA simulation is worth a thousand worlds - Importance sampling Monte Carlo applied to COVID-19 contingency\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and \u003ca href='/article/rs-109108/latest.pdf' target='_blank'\u003e accessed as a PDF.\u003c/a\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Julia Language, Predictive model, Inference, Gen","lastPublishedDoi":"10.21203/rs.3.rs-109108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-109108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCOVID-19 outbreak has become a global pandemic that affected more than 200 countries worldwide. Predicting the behavior of this outbreak has a crucial role in organizing preventive and protective actions, and in improving the decision making process.\u003c/p\u003e\u003cp\u003eThe aim of predicting the number of people who contract the virus has so far been pursued with regression models (exponential, logistics, ...), but regressions can integrate the variable context only a posteriori. The regression models are all dependent on their own history, thus, they can not display anything which did not happen before.\u003c/p\u003e\u003cp\u003eThe pandemic infection of COVID-19 presents a transmission behavior that is widely changing over time. This is due to the growth of the efficiency in the detection of infected, for the changes in social distancing measures and for the widespread use of individual protection devices.\u003c/p\u003e\u003cp\u003eThe approach presented in this paper, starting from the definition of simplified risk assessment framework, aims at designing a probabilistic model for the virus transmission and detection, keeping into account this context changes, binding the correct set of variables to them, and at inferring the distribution for the underlying stochastic variables. This is a key to unlock innovative and valuable insights from the current events. The model has been built in Gen, a probabilistic programming system, built at MIT and embedded in Julia.\u003c/p\u003e","manuscriptTitle":"A simulation is worth a thousand worlds - Importance sampling Monte Carlo applied to COVID-19 contingency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-11-16 23:33:37","doi":"10.21203/rs.3.rs-109108/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"182e1667-5d34-4415-8eb8-550957c06613","owner":[],"postedDate":"November 16th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1092744,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2020-11-16T23:33:38+00:00","versionOfRecord":[],"versionCreatedAt":"2020-11-16 23:33:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-109108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-109108","identity":"rs-109108","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}