Evidence map›Paper›PMID 36437671›Full record

ArticleIndoor air2022

Combining Phi6 as a surrogate virus and computational large-eddy simulations to study airborne transmission of SARS-CoV-2 in a restaurant.

Lotta Oksanen, Mikko Auvinen, Joel Kuula, Rasmus Malmgren, Martin Romantschuk, Antti Hyvärinen, Sirpa Laitinen, Leena Maunula, Enni Sanmark, Ahmed Geneid and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Indoor air, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.3field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 3 institutions in 1 country.

Lotta OksanenDepartment of Otorhinolaryngology and Phoniatrics - Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0002-0344-5078
Mikko AuvinenFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0002-6927-825X
Joel KuulaFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0003-3270-5972
Rasmus MalmgrenFaculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-8570-1134
Martin RomantschukFaculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.ORCID 0000-0003-0473-3327
Antti HyvärinenFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0001-7938-071X
Sirpa LaitinenFinnish Institute of Occupational Health, Helsinki, Finland.
Leena MaunulaFaculty of Veterinary Medicine, Food Hygiene and Environmental Health, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-0841-5353
Enni SanmarkDepartment of Otorhinolaryngology and Phoniatrics - Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0003-0209-5501
Ahmed GeneidDepartment of Otorhinolaryngology and Phoniatrics - Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0003-1617-1181
Svetlana SofievaFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0001-5343-0870
Julija SalokasFaculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-2448-0841
Helin VeskiväliFaculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
Tarja SironenDepartment of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-2344-2755
Tiia GrönholmFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0002-7448-7144
Antti HellstenFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0002-2548-1459
Nina AtanasovaFinnish Meteorological Institute, Helsinki, Finland.ORCID 0000-0003-1120-7405
University of Helsinki · FIFinnish Meteorological Institute · FIFinnish Institute of Occupational Health · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has highlighted the need for indoor risk-reduction strategies. Our aim is to provide information about the virus dispersion and attempts to reduce the infection risk. Indoor transmission was studied simulating a dining situation in a restaurant. Aerosolized Phi6 viruses were detected with several methods. The aerosol dispersion was modeled by using the Large-Eddy Simulation (LES) technique. Three risk-reduction strategies were studied: (1) augmenting ventilation with air purifiers, (2) spatial partitioning with dividers, and (3) combination of 1 and 2. In all simulations infectious viruses were detected throughout the space proving the existence long-distance aerosol transmission indoors. Experimental cumulative virus numbers and LES dispersion results were qualitatively similar. The LES results were further utilized to derive the evolution of infection probability. Air purifiers augmenting the effective ventilation rate by 65% reduced the spatially averaged infection probability by 30%-32%. This relative reduction manifests with approximately 15 min lag as aerosol dispersion only gradually reaches the purifier units. Both viral findings and LES results confirm that spatial partitioning has a negligible effect on the mean infection-probability indoors, but may affect the local levels adversely. Exploitation of high-resolution LES jointly with microbiological measurements enables an informative interpretation of the experimental results and facilitates a more complete risk assessment.

Indexed as

Air Pollution, IndoorCOVID-19HumansRespiratory Aerosols and DropletsRestaurantsSARS-CoV-2aerosol transmissionair purifiersCOVID-19infection-probabilityinfective virusesspace dividers

Identifiers

PMID36437671
PMCPMC10100099
OpenAlexW4310248842

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.