Forecasting for COVID-19 has failed.
Identifieur interne : 000565 ( Main/Exploration ); précédent : 000564; suivant : 000566Forecasting for COVID-19 has failed.
Auteurs : John P A. Ioannidis [États-Unis] ; Sally Cripps [Australie] ; Martin A. Tanner [États-Unis]Source :
- International journal of forecasting [ 0169-2070 ] ; 2020.
Abstract
Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.
DOI: 10.1016/j.ijforecast.2020.08.004
PubMed: 32863495
PubMed Central: PMC7447267
Affiliations:
- Australie, États-Unis
- Californie, Illinois, Nouvelle-Galles du Sud
- Stanford (Californie), Sydney
- Université Stanford
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Ioannidis</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), 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><author><name sortKey="Cripps, Sally" sort="Cripps, Sally" uniqKey="Cripps S" first="Sally" last="Cripps">Sally Cripps</name><affiliation wicri:level="3"><nlm:affiliation>School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments (DARE) Australian Research Council, Sydney, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments (DARE) Australian Research Council, Sydney</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Tanner, Martin A" sort="Tanner, Martin A" uniqKey="Tanner M" first="Martin A" last="Tanner">Martin A. 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Ioannidis</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), 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><author><name sortKey="Cripps, Sally" sort="Cripps, Sally" uniqKey="Cripps S" first="Sally" last="Cripps">Sally Cripps</name><affiliation wicri:level="3"><nlm:affiliation>School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments (DARE) Australian Research Council, Sydney, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments (DARE) Australian Research Council, Sydney</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Tanner, Martin A" sort="Tanner, Martin A" uniqKey="Tanner M" first="Martin A" last="Tanner">Martin A. Tanner</name><affiliation wicri:level="2"><nlm:affiliation>Department of Statistics, Northwestern University, Evanston, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Statistics, Northwestern University, Evanston, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author></analytic><series><title level="j">International journal of forecasting</title><idno type="ISSN">0169-2070</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">Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32863495</PMID><DateRevised><Year>2020</Year><Month>10</Month><Day>06</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0169-2070</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Aug</Month><Day>25</Day></PubDate></JournalIssue><Title>International journal of forecasting</Title><ISOAbbreviation>Int J Forecast</ISOAbbreviation></Journal><ArticleTitle>Forecasting for COVID-19 has failed.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.ijforecast.2020.08.004</ELocationID><Abstract><AbstractText>Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.</AbstractText><CopyrightInformation>© 2020 Published by Elsevier B.V. on behalf of International Institute of Forecasters.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ioannidis</LastName><ForeName>John P A</ForeName><Initials>JPA</Initials><AffiliationInfo><Affiliation>Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cripps</LastName><ForeName>Sally</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments (DARE) Australian Research Council, Sydney, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tanner</LastName><ForeName>Martin A</ForeName><Initials>MA</Initials><AffiliationInfo><Affiliation>Department of Statistics, Northwestern University, Evanston, IL, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>25</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Int J Forecast</MedlineTA><NlmUniqueID>101084857</NlmUniqueID><ISSNLinking>0169-2070</ISSNLinking></MedlineJournalInfo><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="entrez"><Year>2020</Year><Month>9</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32863495</ArticleId><ArticleId IdType="doi">10.1016/j.ijforecast.2020.08.004</ArticleId><ArticleId IdType="pii">S0169-2070(20)30119-9</ArticleId><ArticleId IdType="pmc">PMC7447267</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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(Californie)</li><li>Sydney</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Ioannidis, John P A" sort="Ioannidis, John P A" uniqKey="Ioannidis J" first="John P A" last="Ioannidis">John P A. Ioannidis</name></region><name sortKey="Tanner, Martin A" sort="Tanner, Martin A" uniqKey="Tanner M" first="Martin A" last="Tanner">Martin A. Tanner</name></country><country name="Australie"><region name="Nouvelle-Galles du Sud"><name sortKey="Cripps, Sally" sort="Cripps, Sally" uniqKey="Cripps S" first="Sally" last="Cripps">Sally Cripps</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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