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Record W3048190731 · doi:10.1093/cid/ciaa1118

Re: It Is Time to Address Airborne Transmission of COVID-19

2020· letter· en· W3048190731 on OpenAlexaff
Zain Chagla, Susy Hota, Sarah Khan, Dominik Mertz

Bibliographic record

VenueClinical Infectious Diseases · 2020
Typeletter
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsHamilton Health SciencesUniversity Health NetworkUniversity of TorontoMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakTransmission (telecommunications)Computer scienceVirologyTelecommunicationsMedicineOutbreakInfectious disease (medical specialty)

Abstract

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To the Editor—We are concerned that the commentary by Morowska and Milton [1] has caused significant confusion. We agree that there is a gradient from large droplets to aerosols. We also agree that under experimental conditions and possibly in poorly ventilated, indoor, crowded environments there is potential for the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by aerosols. Furthermore, we agree that the availability of adequate ventilation indoors and the use of outdoor space have validity in preventing transmission. However, we argue that the epidemiologic data and clinical experience in managing the pandemic continue to support that the main mode of SARS-CoV-2 transmission is short range through droplets and close contact [2]. The concerns raised by the authors are not borne out in clinical experience. Long-range transmission beyond 2 meters in the more than 10 000 patients with coronavirus disease 2019 (COVID-19) hospitalized nationally in Canada and elsewhere seems rare at best. Current policies in many international jurisdictions recommend droplet/contact precautions for routine care of patients with suspected or confirmed COVID-19 and the addition of airborne precautions only for aerosol-generating medical procedures (AGMPs) [3]. Epidemiologic studies support this approach and even suggest that AGMP transmission risk may be overestimated [4]. In the case of the healthcare environment, we did not find any convincing evidence in their review to change occupational health and infection control practices. In contrast, real-world experiences have been published where, despite significant aerosol generation, rates of transmissions have been minimal. The first community-acquired COVID-19 case in the United States underwent multiple high-risk prolonged AGMPs [5]. Despite 121 exposures without N95 respirators, only 3 (2.5%) healthcare workers acquired SARS-COV-2, 2 of whom did not wear any respiratory protection at all and the third wore a surgical mask intermittently. In Singapore, 41 healthcare workers were exposed to multiple prolonged AGMPs in a COVID-19 patient, only 6 wore N95 respirators [6]. On serial testing, no staff acquired COVID-19. These observational case reports substantiate the Canadian experience in which COVID-19 patients are routinely managed with droplet/contact precautions; there has been no increased risk of infections in healthcare workers when compared with community populations [7]. Published case series of nonhealthcare settings confirm the findings of droplet/contact transmission, including a flight where only a single adjacent passenger was secondarily infected [8] and a cluster of infections at a call center related to close contact within a building [9], as well as multiple household contact studies with secondary attack rates of less than 20% [10]. Evidence-based policy around infection prevention should be informed by research from the physical sciences, biology, and epidemiology, with consideration of real-life aspects. We commend the authors for highlighting relevant experimental evidence. However, without reconciling with the clinical real-world experience of COVID-19, the authors draw premature conclusions about the importance of airborne transmission. This has resulted in confusion and fear in the general public, mistrust in healthcare workers, and a risk of a deepening divide between experimental scientists and healthcare epidemiologists. Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Potential conflicts of interest. S. H. reports a research study grant from Finch Therapeutics outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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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)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.108
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0090.006
Open science0.0020.004
Research integrity0.1080.057
Insufficient payload (model declined to judge)0.0390.019

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 head0.060
GPT teacher head0.386
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations32
Published2020
Admission routes1
Has abstractno

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