Data-driven modeling of COVID-19-Lessons learned.
Identifieur interne : 000645 ( Main/Exploration ); précédent : 000644; suivant : 000646Data-driven modeling of COVID-19-Lessons learned.
Auteurs : Ellen Kuhl [États-Unis]Source :
- Extreme Mechanics Letters [ 2352-4316 ] ; 2020.
Abstract
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 models for 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.
DOI: 10.1016/j.eml.2020.100921
PubMed: 32837980
PubMed Central: PMC7427559
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Data-driven modeling of COVID-19-Lessons learned.</title><author><name sortKey="Kuhl, Ellen" sort="Kuhl, Ellen" uniqKey="Kuhl E" first="Ellen" last="Kuhl">Ellen Kuhl</name><affiliation wicri:level="4"><nlm:affiliation>Department of Mechanical Engineering, Stanford University, Stanford, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical Engineering, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32837980</idno><idno type="pmid">32837980</idno><idno type="doi">10.1016/j.eml.2020.100921</idno><idno type="pmc">PMC7427559</idno><idno type="wicri:Area/Main/Corpus">000395</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000395</idno><idno type="wicri:Area/Main/Curation">000395</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000395</idno><idno type="wicri:Area/Main/Exploration">000395</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Data-driven modeling of COVID-19-Lessons learned.</title><author><name sortKey="Kuhl, Ellen" sort="Kuhl, Ellen" uniqKey="Kuhl E" first="Ellen" last="Kuhl">Ellen Kuhl</name><affiliation wicri:level="4"><nlm:affiliation>Department of Mechanical Engineering, Stanford University, Stanford, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical Engineering, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></analytic><series><title level="j">Extreme Mechanics Letters</title><idno type="ISSN">2352-4316</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 models for 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.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32837980</PMID><DateRevised><Year>2020</Year><Month>10</Month><Day>06</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">2352-4316</ISSN><JournalIssue CitedMedium="Print"><Volume>40</Volume><PubDate><Year>2020</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Extreme Mechanics Letters</Title><ISOAbbreviation>Extreme Mech Lett</ISOAbbreviation></Journal><ArticleTitle>Data-driven modeling of COVID-19-Lessons learned.</ArticleTitle><Pagination><MedlinePgn>100921</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.eml.2020.100921</ELocationID><Abstract><AbstractText>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 models for 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.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kuhl</LastName><ForeName>Ellen</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Mechanical Engineering, Stanford University, Stanford, CA, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U01 HL119578</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Extreme Mech Lett</MedlineTA><NlmUniqueID>101658429</NlmUniqueID><ISSNLinking>2352-4316</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian inference</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Data-driven modeling</Keyword><Keyword MajorTopicYN="N">Epidemiology</Keyword><Keyword MajorTopicYN="N">Extreme diffusion</Keyword><Keyword MajorTopicYN="N">Extreme growth</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>07</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>08</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32837980</ArticleId><ArticleId IdType="doi">10.1016/j.eml.2020.100921</ArticleId><ArticleId IdType="pii">S2352-4316(20)30178-4</ArticleId><ArticleId IdType="pii">100921</ArticleId><ArticleId IdType="pmc">PMC7427559</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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