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Record W3040037836 · doi:10.7326/l20-0592

Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From Iran

2020· letter· en· W3040037836 on OpenAlexaffabout
Hamid Sharifi, Mohammad Karamouzian, Zahra Khorrami, Malahat Khalili, Ehsan Mostafavi, Sana Eybpoosh, Ali Mirzazadeh, Ali Akbar Haghdoost

Bibliographic record

VenueAnnals of Internal Medicine · 2020
Typeletter
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFamily medicineLibrary science

Abstract

fetched live from OpenAlex

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 ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.214
GPT teacher head0.474
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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