Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From Iran
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Résumé
Letters7 July 2020Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From IranFREEHamid Sharifi, DVM, PhD, Mohammad Karamouzian, DVM, MSc, Zahra Khorrami, MSc, Malahat Khalili, MSc, Ehsan Mostafavi, DVM, PhD, Sana Eybpoosh, MSc, PhD, Ali Mirzazadeh, MD, PhD, and Ali Akbar Haghdoost, MD, PhDHamid Sharifi, DVM, PhDHIV/STI Surveillance Research Center and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (H.S., Z.K., M.K.), Mohammad Karamouzian, DVM, MScUniversity of British Columbia, Vancouver, British Columbia, Canada (M.K.), Zahra Khorrami, MScHIV/STI Surveillance Research Center and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (H.S., Z.K., M.K.), Malahat Khalili, MScHIV/STI Surveillance Research Center and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (H.S., Z.K., M.K.), Ehsan Mostafavi, DVM, PhDResearch Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran (E.M., S.E.), Sana Eybpoosh, MSc, PhDResearch Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran (E.M., S.E.), Ali Mirzazadeh, MD, PhDUniversity of California, San Francisco, San Francisco, California, United States of America (A.M.), and Ali Akbar Haghdoost, MD, PhDModeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (A.A.H.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-0592 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:We read Tuite and colleagues' recent mathematical modeling study (1) with interest. However, we are concerned about the accuracy of the reported estimates and their underlying assumptions.First, the authors assumed the onset date of the epidemic to be early January 2020 without providing any evidence. In the last week of February, when the study was done, Iran was not even among the top 50 international destinations from different cities in China; it is therefore unlikely that the epidemic in Iran started in early January (2). Moreover, relying on data from the United Nations World Tourism Organization to estimate the proportion of international travelers who are residents of Iran, as well as the average length of tourists' stay in Iran, is problematic because these data do not provide the number of days that people infected with severe acute respiratory syndrome coronavirus 2 were actually in Iran. Because the incubation period of this virus ranges between 2 and 14 days, with possible outliers of up to 27 days (3), it is unclear whether the travelers identified in other countries wereinfected in Iran or were already infected before their last stay in the country.Second, the Infectious Disease Vulnerability Index used to estimate Iran's outbreak response capacity is a tool to provide international agencies with a better understanding of countries' vulnerability to infectious disease outbreaks in “normal” situations. It therefore underestimates their capacities during outbreaks, when surveillance systems are much more sensitive and case detection is enhanced.Finally, the authors assumed a similar prevalence of coronavirus disease 2019 (COVID-19) in cities with and without international airports; however, the chance of exposure to COVID-19 through national or international travel is uneven across these cities (4). Approximately 30% of Iran's population lives in rural areas. Furthermore, only 13 of the 54 airports in Iran are international airports, and these are located in 12 of the country's 434 cities (5). The assumptions we have noted here would have caused an overestimation of the overall number of patients with COVID-19 in Iran in this study.There are substantial uncertainties about the magnitude of the COVID-19 epidemic in Iran, and several surveillance studies and epidemiologic field investigations are ongoing to help provide more reliable estimates. Although mathematical models of COVID-19 might provide some insight for COVID-19 response planning and decision making in Iran, they may be misleading if not viewed with a critical eye for their limitations and subjective assumptions.References1. Tuite AR, Bogoch II, Sherbo R, et al. Estimation of coronavirus disease 2019 (COVID-19) burden and potential for international dissemination of infection from Iran [Letter]. Ann Intern Med. 2020;172:699-701. [PMID: 32176272]. doi:10.7326/M20-0696 LinkGoogle Scholar2. International Air Transport Association. Annual Review 2019. 2019. Accessed at www.iata.org/contentassets/c81222d96c9a4e0bb4ff6ced0126f0bb/iata-annual-review-2019.pdf on 13 March 2020. Google Scholar3. Guan WJ, Ni ZY, Hu Y, et al; China Medical Treatment Expert Group for Covid-19.. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708-1720. [PMID: 32109013] doi:10.1056/NEJMoa2002032 CrossrefMedlineGoogle Scholar4. Fraser C, Donnelly CA, Cauchemez S, et al; WHO Rapid Pandemic Assessment Collaboration.. Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009;324:1557-1561. [PMID: 19433588] doi:10.1126/science.1176062 CrossrefMedlineGoogle Scholar5. Ministry of Roads and Urban Development, Iran Airports and Air Navigation Company. 2020. Accessed at https://statistics.airport.ir on 17 March 2020. Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Hamid Sharifi, DVM, PhD; Mohammad Karamouzian, DVM, MSc; Zahra Khorrami, MSc; Malahat Khalili, MSc; Ehsan Mostafavi, DVM, PhD; Sana Eybpoosh, MSc, PhD; Ali Mirzazadeh, MD, PhD; Ali Akbar Haghdoost, MD, PhDAffiliations: HIV/STI Surveillance Research Center and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (H.S., Z.K., M.K.)University of British Columbia, Vancouver, British Columbia, Canada (M.K.)Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran (E.M., S.E.)University of California, San Francisco, San Francisco, California, United States of America (A.M.)Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran (A.A.H.)Note: Dr. Haghdoost is the Deputy Minister in Education of the Ministry of Health of Iran.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L20-0592. 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 Ashleigh R. Tuite , Isaac I. Bogoch , and David Fisman Metrics 7 July 2020Volume 173, Issue 1 Page: 73-74 Keywords COVID-19 Decision making Disclosure Epidemiology Infectious disease surveillance Infectious diseases Mathematical models Rural areas Upper respiratory tract infections 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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».