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dc.creatorKuhl, Ellen
dc.date.accessioned2020-09-01T15:00:10Z
dc.date.available2020-09-01T15:00:10Z
dc.date.created2020
dc.identifier.issn2352-4316spa
dc.identifier.otherhttps://doi.org/10.1016/j.eml.2020.100921spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/12537
dc.description.abstractUnderstanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported cases across 188 countries with more than 700,000 deaths worldwide. Unlike any other disease in history, COVID-19 has generated an unprecedented volume of data, well documented, continuously updated, and broadly available to the general public. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from six month of modeling COVID-19. We highlight the early success of classical modelsfor infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data to make informed predictions and guide political decision making. We critically discuss questions that these models can and cannot answer and showcase controversial decisions around the early outbreak dynamics, outbreak control, and exit strategies. We anticipate that this summary will stimulate discussion within the modeling community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic. EML webinar speakers, videos, and overviews are updated at https://imechanica.org/node/24098spa
dc.format.extent21 páginasspa
dc.format.mimetypetext/htmlspa
dc.language.isoengspa
dc.publisherExtreme Mechanics Lettersspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCOVID-19spa
dc.subjectData-driven modelingspa
dc.subjectBayesian inferencespa
dc.subjectEpidemiologyspa
dc.subjectExtreme diffusionspa
dc.subjectExtreme growthspa
dc.titleData-driven modeling of COVID-19—Lessons learnedspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAcceso restringidospa
dc.identifier.doihttps://doi.org/10.1016/j.eml.2020.100921spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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