Estimation of Coronavirus Disease 2019 Burden and Potential for International Dissemination of Infection From Iran
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".