Evidence map›Paper›PMID 40477856›Full record

ArticleJournal of building engineering2022

A practical approach for preventing dispersion of infection disease in naturally ventilated room.

Chen Ren, Shi-Jie Cao, Fariborz Haghighat

Abstract read
In one paragraph

Article in Journal of building engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

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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

3 authors.

Chen RenSchool of Architecture, Southeast University, 2 Sipailou, Nanjing, 210096, China.
Shi-Jie CaoSchool of Architecture, Southeast University, 2 Sipailou, Nanjing, 210096, China.
Fariborz HaghighatEnergy and Environment Group, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, H3G 1M8, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the ongoing COVID-19 pandemic period, the airborne transmission of viruses has raised widespread concern as daily activities are resumed in public buildings. It is essential to develop mitigation strategies of infection disease transmission (e.g., increase of ventilation rate) in different scenarios to reduce the infection risk. For classrooms in schools, natural ventilation is generally used to provide outdoor air into rooms. However, the supply air volume depends strongly on the local conditions, e.g., window opening size and outdoor wind speed. In this study, the optimal design of classroom window openings is investigated, based on which low-cost window-integrated fans are then employed to enhance the efficiency of natural ventilation and infection disease control. Taking infected students as pollutant sources, numerical simulations are carried out to predict the pollutant concentration under various scenarios of pollutant sources and window opening modes (with/without fans), and to calculate the infection risk. It is found that by redesigning window openings, the airflow distribution performance index (ADPI) can be increased by 17% with corresponding infection likelihood decreased by 27%. The window-integrated fan has a significant effect on improving ventilation performance and prevention of infection disease transmission, leading to an ADPI of 99% and minimum infection probability of 11% for students sitting near the windows. This work can help to develop low-cost and effective mitigating measures of infection disease in classrooms by using hybrid ventilation systems.

Indexed as

ClassroomCOVID-19Infection riskNatural ventilationWindow-integrated fan

Identifiers

PMID40477856
PMCPMC8686462

What OpenQuestion holds

Textmetadata
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.