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Record W4308552893 · doi:10.2196/37232

The Impact of Heating, Ventilation, and Air-Conditioning Design Features on the Transmission of Viruses, Including SARS-CoV-2: Overview of Reviews

2022· review· en· W4308552893 on OpenAlexaffvenue
Gail M. Thornton, Emily Kroeker, Brian A. Fleck, Lexuan Zhong, Lisa Hartling

Bibliographic record

VenueInteractive Journal of Medical Research · 2022
Typereview
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVentilation (architecture)Air conditioningCoronavirus disease 2019 (COVID-19)Transmission (telecommunications)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Airborne transmissionArchitectural engineeringConditioning2019-20 coronavirus outbreakEnvironmental scienceVirologyEngineeringMechanical engineeringTelecommunicationsMedicineMathematicsStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 or SARS-CoV-2 outbreak was declared a pandemic by the World Health Organization in March 2020. Almost 2 years later (early February 2022), the World Health Organization reported over 383 million cases of the disease caused by the virus, with over 5.6 million deaths worldwide. Debate regarding the routes of transmission was substantial early in the pandemic; however, airborne transmission emerged as an important consideration. Infectious airborne agents can spread within the built environment through heating, ventilation, and air-conditioning (HVAC) systems. Multiple features of HVAC systems can influence transmission (eg, ventilation, filtration, UV radiation, and humidity). Understanding how HVAC features influence airborne transmission is critical to mitigate the spread of infectious agents. OBJECTIVE: Given the airborne transmission of SARS-CoV-2, an overview of reviews was conducted to understand what is already known from the scientific literature about how virus transmission may be affected by HVAC design features in the built environment. METHODS: Ovid MEDLINE and Compendex were searched from inception to January 2021. Two reviewers independently screened the titles, abstracts, and full text of potentially relevant reviews, using a priori inclusion criteria: systematic reviews examining the effects of HVAC design features on virus transmission. Two reviewers independently assessed the methodological quality using AMSTAR2. RESULTS: Searching identified 361 citations, of which 45 (12.5%) were potentially relevant and 7 (2%) were included. Reviews were published between 2007 and 2021 and included 47 virus studies. Two earlier reviews (2007 and 2016) of 21 studies found sufficient evidence that mechanical ventilation (airflow patterns and ventilation rates) plays a role in airborne transmission; however, both found insufficient evidence to quantify the minimum mechanical ventilation requirements. One review (2017) of 9 studies examining humidity and indoor air quality found that influenza virus survival was lowest between 40% and 80% relative humidity; the authors noted that ventilation rates were a confounding variable. Two reviews (2021) examined mitigation strategies for coronavirus transmission, finding that transmission decreased with increasing temperature and relative humidity. One review (2020) identified 14 studies examining coronavirus transmission in air-conditioning systems, finding that HVAC systems played a role in virus spread during previous coronavirus outbreaks. One review (2020) examined virus transmission interventions in public ground transportation, finding ventilation and filtration to be effective. CONCLUSIONS: Seven reviews synthesizing 47 studies demonstrated a role for HVAC in mitigating airborne virus transmission. Ventilation, humidity, temperature, and filtration can play a role in the viability and transmission of viruses, including coronaviruses. Recommendations for minimum standards were not possible owing to few studies investigating a given HVAC parameter. This overview examining HVAC design features and their effects on the airborne transmission of viruses serves as a starting point for future systematic reviews and identifying priorities for primary research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.389
GPT teacher head0.573
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2022
Admission routes2
Has abstractyes

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