Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From Iran
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Letters7 July 2020Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From IranFREEAshleigh R. Tuite, PhD, MPH, Isaac I. Bogoch, MD, and David Fisman, MD, MPHAshleigh R. Tuite, PhD, MPHUniversity of Toronto, Toronto, Ontario, Canada (A.R.T., D.F.)Search for more papers by this author, Isaac I. Bogoch, MDUniversity of Toronto and University Health Network, Toronto, Ontario, Canada (I.I.B.)Search for more papers by this author, and David Fisman, MD, MPHUniversity of Toronto, Toronto, Ontario, Canada (A.R.T., D.F.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-0593 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We appreciate Dr. Sharifi and colleagues' thoughtful comments and concerns. We agree that models are simplified representations of reality and are limited by the data used to parameterize them. In our analysis, we assumed that COVID-19 had been circulating in Iran for 1.5 months at the time of our analysis in late February, which would be consistent with an initial case introduction in early to mid-January. In support of this assumption, data now suggest that there was rapid global dissemination of COVID-19 cases in January (before travel restrictions were implemented on 23 January) that was undetected because of the high prevalence of mildly symptomatic or asymptomatic infections (1). The use of data on average tourist behaviors was a required simplification and represented the best available data. We conducted multiple sensitivity analyses, and even our highly conservative estimate of the epidemic size in Iran—which assumed no undetected exported COVID-19 cases among all outbound air passengers—was more than 40 times the officially reported numbers at that time.Dr. Sharifi and colleagues mistakenly assert that we used the Infectious Disease Vulnerability Index to estimate Iran's outbreak response capacity. We actually used this index to highlight other countries with high connectivity to Iran via air travel that would benefit from heightened surveillance. We concur that such a metric may not fully capture a country's capacity to respond to public health threats, especially in the midst of a public health emergency. However, we contend that it is useful for stratifying risk and identifying particularly vulnerable countries when used in conjunction with other data, as was done in our analysis.In conclusion, we recognize the limitations associated with our analysis, which mainly relate to simplifying assumptions. Despite these limitations, the key finding of our study has been validated by abundant observations consistent with a large COVID-19 epidemic in Iran (2, 3), including the appearance of new large burial sites there that became visible on satellite imagery after the epidemic began in that country (4). Our model results are one further piece of evidence lending support to this conclusion.References1. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020;368:489-493. [PMID: 32179701] doi:10.1126/science.abb3221 CrossrefMedlineGoogle Scholar2. Wood G. Iran has far more coronavirus cases than it is letting on. The Atlantic. 9 March 2020. Accessed at www.theatlantic.com/ideas/archive/2020/03/irans-coronavirus-problem-lot-worse-it-seems/607663 on 1 May 2020. Google Scholar3. Zhuang Z, Zhao S, Lin Q, et al. Preliminary estimation of the novel coronavirus disease (COVID-19) cases in Iran: a modelling analysis based on overseas cases and air travel data. Int J Infect Dis. 2020;94:29-31. [PMID: 32171951] doi:10.1016/j.ijid.2020.03.019 CrossrefMedlineGoogle Scholar4. Borger J. Satellite images show Iran has built mass graves amid coronavirus outbreak. The Guardian. 12 March 2020. Accessed at www.theguardian.com/world/2020/mar/12/coronavirus-iran-mass-graves-qom on 1 May 2020. Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Ashleigh R. Tuite, PhD, MPH; Isaac I. Bogoch, MD; David Fisman, MD, MPHAffiliations: University of Toronto, Toronto, Ontario, Canada (A.R.T., D.F.)University of Toronto and University Health Network, Toronto, Ontario, Canada (I.I.B.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-0696. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEstimation of Coronavirus Disease 2019 (COVID-19) Burden and Potential for International Dissemination of Infection From Iran Ashleigh R. Tuite , Isaac I. Bogoch , Ryan Sherbo , Alexander Watts , David Fisman , and Kamran Khan Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From Iran Hamid Sharifi , Mohammad Karamouzian , Zahra Khorrami , Malahat Khalili , Ehsan Mostafavi , Sana Eybpoosh , Ali Mirzazadeh , and Ali Akbar Haghdoost Metrics Cited byA Comprehensive Comparison of COVID-19 Characteristics (Wuhan Strain) Between Children and Adults During Initial Pandemic Phase: A Meta-Analysis Study 7 July 2020Volume 173, Issue 1 Page: 74-75 Keywords Behavior COVID-19 Disclosure Infectious disease surveillance Infectious diseases Prevention, policy, and public health ePublished: 7 July 2020 Issue Published: 7 July 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».