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...
- 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
- 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 |
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oai_identifier_str |
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12537 |
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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 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
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 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_f1cf |
dc.rights.local.spa.fl_str_mv |
Acceso restringido |
rights_invalid_str_mv |
Acceso restringido http://purl.org/coar/access_right/c_f1cf |
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 |
bitstream.url.fl_str_mv |
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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; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12537/2/license.txtabceeb1c943c50d3343516f9dbfc110fMD52open access20.500.12010/12537oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/125372020-09-01 10:00:10.591open accessRepositorio Institucional - Universidad Jorge Tadeo Lozanoexpeditio@utadeo.edu.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 |