Evidence map›Paper›PMID 40478136›Full record

ArticleJournal of building engineering2023

Investigation on the cross-infection control performance of interactive cascade ventilation in multi-scenario of winter.

Han Li, Yuer Lan, Xiuqin Ma, Xiangfei Kong, Man Fan

Abstract read
In one paragraph

Article in Journal of building engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Han LiSchool of Energy and Environmental Engineering, Hebei University of Technology, China.
Yuer LanSchool of Energy and Environmental Engineering, Hebei University of Technology, China.
Xiuqin MaSchool of Energy and Environmental Engineering, Hebei University of Technology, China.
Xiangfei KongSchool of Energy and Environmental Engineering, Hebei University of Technology, China.
Man FanSchool of Energy and Environmental Engineering, Hebei University of Technology, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the wide spread of COVID-19, numerous cases demonstrate that proper ventilation method can reduce the cross-infection risk obviously. Interactive cascade ventilation (ICV) as a recently proposed ventilation method, the advantage of indoor environment construction has been proven. However, few studies are conducted to investigate the virus prevention and control characteristics of ICV, which is particularly important under epidemic normalizing. Hence, this study explored and compared the cross-infection control performance of three ventilation strategies, namely mixing ventilation (MV), stratum ventilation (SV), and interactive cascade ventilation (ICV), with a validated CFD model. A typical office was selected as the background scene, where an infected person coughs, sneezes with standing or sitting at different positions. Exposure doses, health infection risk, and disease burden (DB) were employed as the evaluation indicators under different ventilation methods of multi-scenario. The research results indicated that the average aerosol exposure dose among the human respiratory region under ICV was 0.29 g/day, which was reduced by 67 % and 50 % compared with MV and SV. In addition, only in ICV can the health infection risk meets the EPA standard. The average disease health burden for exposed persons under ICV was 0.93

Indexed as

COVID-19Cross-infection riskInteractive cascade ventilationVentilation strategiesVirus prevention and control performance

Identifiers

PMID40478136
PMCPMC9753475

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.