ArticleProceedings. Mathematical, physical, and engineering sciences2022
Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- Comparative assessment of airborne infection risk tools in enclosed spaces: Implications for disease control.Infectious Disease Modelling · 2025Article
- Modelling indoor airborne transmission combining architectural design and people movement using the VIRIS simulator and web app.Scientific reports · 2024Article
- Comparing strategies for the mitigation of SARS-CoV-2 airborne infection risk in tiered auditorium venues.Communications engineering · 2024Article
- Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map.Journal of public health (Oxford, England) · 2024Review
- The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model.PLoS computational biology · 2024Article
- VisualPDE: Rapid Interactive Simulations of Partial Differential Equations.Bulletin of mathematical biology · 2023Article
- Response to the COVID-19 Pandemic in Classrooms at the University of the Basque Country through a User-Informed Natural Ventilation Demonstrator.International journal of environmental research and public health · 2022Article
- Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Indoor transmission of airborne viral aerosol with a simplistic reaction-diffusion model.The European physical journal. Special topics · 2022Article
- On the utility of a well-mixed model for predicting disease transmission on an urban bus.AIP advances · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We develop a spatially dependent generalization to the Wells-Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection-diffusion-reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies-a courtroom, a restaurant, and a hospital ward-demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic.
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Registered trials
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.