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.
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.
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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.
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