Data-driven modeling of COVID-19—Lessons learned

Understanding 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 othe...

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Autores:
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
Idioma:
eng
OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12537
Acceso en línea:
https://doi.org/10.1016/j.eml.2020.100921
http://hdl.handle.net/20.500.12010/12537
Palabra clave:
COVID-19
Data-driven modeling
Bayesian inference
Epidemiology
Extreme diffusion
Extreme growth
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
Rights
License
Acceso restringido
id UTADEO2_0bb8ca1fa34786ada4c965691ede2ebe
oai_identifier_str oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12537
network_acronym_str UTADEO2
network_name_str Expeditio: repositorio UTadeo
repository_id_str
dc.title.spa.fl_str_mv Data-driven modeling of COVID-19—Lessons learned
title Data-driven modeling of COVID-19—Lessons learned
spellingShingle Data-driven modeling of COVID-19—Lessons learned
COVID-19
Data-driven modeling
Bayesian inference
Epidemiology
Extreme diffusion
Extreme growth
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
title_short Data-driven modeling of COVID-19—Lessons learned
title_full Data-driven modeling of COVID-19—Lessons learned
title_fullStr Data-driven modeling of COVID-19—Lessons learned
title_full_unstemmed Data-driven modeling of COVID-19—Lessons learned
title_sort Data-driven modeling of COVID-19—Lessons learned
dc.subject.spa.fl_str_mv COVID-19
Data-driven modeling
Bayesian inference
Epidemiology
Extreme diffusion
Extreme growth
topic COVID-19
Data-driven modeling
Bayesian inference
Epidemiology
Extreme diffusion
Extreme growth
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
dc.subject.lemb.spa.fl_str_mv Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
description Understanding 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/24098
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-09-01T15:00:10Z
dc.date.available.none.fl_str_mv 2020-09-01T15:00:10Z
dc.date.created.none.fl_str_mv 2020
dc.type.local.spa.fl_str_mv Artículo
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format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.issn.spa.fl_str_mv 2352-4316
dc.identifier.other.spa.fl_str_mv https://doi.org/10.1016/j.eml.2020.100921
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12010/12537
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.eml.2020.100921
identifier_str_mv 2352-4316
url https://doi.org/10.1016/j.eml.2020.100921
http://hdl.handle.net/20.500.12010/12537
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.local.spa.fl_str_mv Acceso restringido
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dc.format.extent.spa.fl_str_mv 21 páginas
dc.format.mimetype.spa.fl_str_mv text/html
dc.publisher.spa.fl_str_mv Extreme Mechanics Letters
dc.source.spa.fl_str_mv reponame:Expeditio Repositorio Institucional UJTL
instname:Universidad de Bogotá Jorge Tadeo Lozano
instname_str Universidad de Bogotá Jorge Tadeo Lozano
institution Universidad de Bogotá Jorge Tadeo Lozano
reponame_str Expeditio Repositorio Institucional UJTL
collection Expeditio Repositorio Institucional UJTL
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spelling 2020-09-01T15:00:10Z2020-09-01T15:00:10Z20202352-4316https://doi.org/10.1016/j.eml.2020.100921http://hdl.handle.net/20.500.12010/12537https://doi.org/10.1016/j.eml.2020.100921Understanding 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/2409821 páginastext/htmlengExtreme Mechanics Lettersreponame:Expeditio Repositorio Institucional UJTLinstname:Universidad de Bogotá Jorge Tadeo LozanoCOVID-19Data-driven modelingBayesian inferenceEpidemiologyExtreme diffusionExtreme growthSíndrome respiratorio agudo graveCOVID-19SARS-CoV-2CoronavirusData-driven modeling of COVID-19—Lessons learnedArtículohttp://purl.org/coar/resource_type/c_2df8fbb1Acceso restringidohttp://purl.org/coar/access_right/c_f1cfKuhl, EllenTHUMBNAILCaptura.PNGCaptura.PNGPortadaimage/png124513https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12537/4/Captura.PNGdadad97dcb68e11ab9796e08a900e2c3MD54open accessData-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdf.jpgData-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdf.jpgIM Thumbnailimage/jpeg7349https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12537/5/Data-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdf.jpg6111e59797ec5463846848b163609efbMD55open accessORIGINALCaptura.PNGCaptura.PNGVer portadaimage/png124513https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12537/1/Captura.PNGdadad97dcb68e11ab9796e08a900e2c3MD51open accessData-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdfData-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdfArtículo reservadoapplication/pdf1966700https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12537/3/Data-driven-modeling-of-COVID-19-Lessons-learne_2020_Extreme-Mechanics-Lette.pdf42c4c821e0c9b2d6bd85ca76c99e1b10MD53embargoed access|||2200-09-01LICENSElicense.txtlicense.txttext/plain; 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