Evidence map›Paper›PMID 34200380›Full record

ArticleSensors (Basel, Switzerland)2021

Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network.

Sepehr Makhsous, Joelle M Segovia, Jiayang He, Daniel Chan, Larry Lee, Igor V Novosselov, Alexander V Mamishev

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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
4.7field-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, 24 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate.International journal of environmental research and public health · 2022
    Review
  6. Article
  7. Article
  8. Article
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

7 authors at 2 institutions in 1 country.

Sepehr MakhsousDepartment of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA.ORCID 0000-0002-3618-9736
Joelle M SegoviaDepartment of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA.
Jiayang HeDepartment of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA.
Daniel ChanSchool of Dentistry, University of Washington, 1959 NE Pacific St., B-307, Seattle, WA 98195, USA.ORCID 0000-0002-7685-493X
Larry LeePacific Industrial Hygiene, LLC, 5520 106th Ave NE, Kirkland, WA 98033, USA.
Igor V NovosselovDepartment of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA.ORCID 0000-0002-6347-7450
Alexander V MamishevDepartment of Electrical and Computer Engineering, University of Washington, Paul Allen Center, 185 E Stevens Way NE AE100R, Seattle, WA 98195, USA.
University of Washington · USPacific Environment · US

Funding

CoMotion, University of Washington 68-2989
6 · The paper itself

Abstract

Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.

Indexed as

COVID-19AerosolsHumansPandemicsParticulate MatterSARS-CoV-2AerosolsParticulate Matteraerosolsair qualitydental clinicsdispersion modelingexposure assessmentextra-oral suction devicehigh-volume evacuationinfection controlparticle concentrationsensor network

Identifiers

PMID34200380
PMCPMC8201307
OpenAlexW3157497576

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.