Evidence map›Paper›PMID 34954184›Full record

ArticleThe Science of the total environment2022

A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces.

Xiangdong Li, Daniel Lester, Gary Rosengarten, Craig Aboltins, Milan Patel, Ivan Cole

Open access · greenAbstract read
In one paragraph

Article in The Science of the total environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
8.1field-weighted citation impact, top 2% 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

20 citing papers in PubMed, 79 citations in OpenAlex.

  1. Article
  2. Article
  3. Risk assessment of infection of COVID-19 contacts based on scenario simulation.Risk analysis : an official publication of the Society for Risk Analysis · 2025
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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

6 authors at 2 institutions in 1 country.

Xiangdong LiSchool of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Daniel LesterSchool of Engineering, RMIT University, Melbourne, VIC 3000, Australia. Electronic address: daniel.lester@rmit.edu.au.
Gary RosengartenSchool of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Craig AboltinsDepartment of Infectious Diseases, Northern Health, Epping, VIC 3076, Australia.
Milan PatelSchool of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Ivan ColeSchool of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
RMIT University · AUNorthern Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The classic Wells-Riley model is widely used for estimation of the transmission risk of airborne pathogens in indoor spaces. However, the predictive capability of this zero-dimensional model is limited as it does not resolve the highly heterogeneous spatiotemporal distribution of airborne pathogens, and the infection risk is poorly quantified for many pathogens. In this study we address these shortcomings by developing a novel spatiotemporally resolved Wells-Riley model for prediction of the transmission risk of different COVID-19 variants in indoor environments. This modelling framework properly accounts for airborne infection risk by incorporating the latest clinical data regarding viral shedding by COVID-19 patients and SARS-CoV-2 infecting human cells. The spatiotemporal distribution of airborne pathogens is determined via computational fluid dynamics (CFD) simulations of airflow and aerosol transport, leading to an integrated model of infection risk associated with the exposure to SARS-CoV-2, which can produce quantitative 3D infection risk map for a specific SARS-CoV-2 variant in a given indoor space. Application of this model to airborne COVID-19 transmission within a hospital ward demonstrates the impact of different virus variants and respiratory PPE upon transmission risk. With the emergence of highly contagious SARS-CoV-2 variants such as the Delta and Omicron strains, respiratory PPE alone may not provide effective protection. These findings suggest a combination of optimal ventilation and respiratory PPE must be developed to effectively control the transmission of COVID-19 in healthcare settings and indoor spaces in general. This generalised risk estimation framework has the flexibility to incorporate further clinical data as such becomes available, and can be readily applied to consider a wide range of factors that impact transmission risk, including location and movement of infectious persons, virus variant and stage of infection, level of PPE and vaccination of infectious and susceptible individuals, impacts of coughing, sneezing, talking and breathing, and natural and mechanised ventilation and filtration.

Indexed as

COVID-19SARS-CoV-2AerosolsHumansVentilationAerosolsCovid-19Delta variantSARS-CoV-2Spatiotemporal infection riskWells-Riley model

Identifiers

PMID34954184
PMCPMC8695516
OpenAlexW4200231105

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.