Evidence map›Paper›PMID 42528337›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2026

Mathematical Modeling of Viral RNA Copies in Indoor Environments: Pre-Processor for Risk Assessment and Decision-Making Support Tool for Public Health.

Seungjae Gwak, Seongmin Cho, David Y H Pui, Dong-Bin Kwak

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Seungjae GwakDepartment of Manufacturing Systems and Design Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0003-8738-9126
Seongmin ChoDepartment of Manufacturing Systems and Design Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.
David Y H PuiDepartment of Mechanical Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Dong-Bin KwakDepartment of Manufacturing Systems and Design Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-4256-769X

Funding

Korea Institute for Advancement of Technology RS-2025-02304112
6 · The paper itself

Abstract

Quantifying human exposure to airborne respiratory viruses is essential for infection risk assessment modeling and evidence-based infection control policies. However, accurate size and time resolved estimates of airborne viral RNA copies in indoor spaces are often hindered by existing models that compress complex droplet physics into too simplified processes. Therefore, we present a size-resolved framework that couples saliva-specific evaporation and residue formation with gravitational settling to compute airborne viral RNA covering 1-2000 µm under varied temperature, relative humidity, and ambient pressure (101.325 and 75.3 kPa). Our framework also combines particle volume contribution considering saliva mass uncertainty, viral load, viral half-life, exhalation mechanisms (talking, coughing, sneezing), and ventilation effects. Under literature-based half-life inputs and volume-proportional RNA allocation, viral load and size-resolved airborne lifetime dominated the simulated RNA-copy trends, although the role of viral half-life may vary if RNA decay differs from infectivity decay or if viral RNA concentrates in long-suspended fine aerosols. Talking dominates persistence of airborne RNA copies since coughing and sneezing produce larger droplets that settle quickly. Also, model predictions are compared with hospital air-sampling datasets. The 50-percentile of saliva mass provides the closest match, with most samples falling within roughly one order of magnitude and central tendency close to unbiased. Our model is designed to serve as (1) pre-processor that supplies inputs for infection-risk assessment modeling studies and (2) decision-making support tool, facilitating public-health intervention. Therefore, we also provide an online web tool that allows users to directly adjust inputs and immediately analyze scenario-specific outcomes.

Indexed as

Air MicrobiologyAir Pollution, IndoorModels, TheoreticalPublic HealthRNA, ViralDecision MakingHumansRisk AssessmentSalivaViral LoadRNA, Viralairborne virus transmissionbio‐aerosolexposure assessmentsaliva droplet airborne lifetimeviral RNA copies

Identifiers

PMID42528337
PMCPMC13420807

What OpenQuestion holds

Textmetadata
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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.