Evidence map›Paper›PMID 35133673›Full record

ReviewIndoor air2022

Modeling the factors that influence exposure to SARS-CoV-2 on a subway train carriage.

Daniel Miller, Marco-Felipe King, James Nally, Joseph R Drodge, Gary I Reeves, Andrew M Bate, Henry Cooper, Ursula Dalrymple, Ian Hall, Martín López-García and 2 more

Abstract readReview
In one paragraph

Review in Indoor air, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The Wells-Riley Model Revisited II: Parameter Uncertainty and Population Heterogeneity.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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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

12 authors.

Daniel MillerDefence Science and Technology Laboratory, Salisbury, UK.
Marco-Felipe KingSchool of Civil Engineering, University of Leeds, Leeds, UK.ORCID 0000-0001-7010-476X
James NallyDefence Science and Technology Laboratory, Salisbury, UK.
Joseph R DrodgeDefence Science and Technology Laboratory, Salisbury, UK.
Gary I ReevesDefence Science and Technology Laboratory, Salisbury, UK.
Andrew M BateSchool of Civil Engineering, University of Leeds, Leeds, UK.
Henry CooperDefence Science and Technology Laboratory, Salisbury, UK.
Ursula DalrympleDefence Science and Technology Laboratory, Salisbury, UK.
Ian HallDepartment of Mathematics, University of Manchester, Manchester, UK.
Martín López-GarcíaSchool of Mathematics, University of Leeds, Leeds, UK.
Simon T ParkerDefence Science and Technology Laboratory, Salisbury, UK.
Catherine J NoakesSchool of Civil Engineering, University of Leeds, Leeds, UK.

Funding

Department of Transport, UK GovernmentEngineering and Physical Sciences Research Council
6 · The paper itself

Abstract

We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS-CoV-2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close-range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask-wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close-range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates.

Indexed as

Air Pollution, IndoorCOVID-19RailroadsAerosolsAir MicrobiologyFomitesHumansSARS-CoV-2Aerosolsairborneclose-rangefomiteSARS-CoV-2 modelingsubwaysubway train carriage

Identifiers

PMID35133673
PMCPMC9111599

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.