Quantifying quanta: Determining emission rates from clinical data Article Swipe
Benjamin Jones
,
Christopher Iddon
,
Max H. Sherman
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.indenv.2024.100025
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.indenv.2024.100025
It is important to quantify uncertainty in the viable genomic material encapsulated in the respiratory particles emitted by infected people so that it can be converted into an emission rate as a function of respiratory and metabolic activities and used to estimate the probability of infection for an indoor scenario. Clinical measurements of viral loads for SARS-CoV-2 made using infection surveys, Gesundheit-II samplers, and human challenge studies are evaluated and a mathematical model is derived to estimate the quantum emission rate as a function of the genomic and viable viral loads. Modelled emission rates for SARS-CoV-2 agree with clinical data above detection limits. The viral load is found to vary over at least 6 orders of magnitude because it is person and time dependent, and contingent on many other factors that are difficult to quantify. It is similarly large for other respiratory pathogens. Therefore, the genomic and viable-virion emission rates display similar heterogeneity. When emission rates are used to estimate absolute infection risk using the Wells-Riley model, the predictions are so uncertain that they cannot be used in any meaningful way to provide useful quantitative guidance for designing indoor spaces.
Related Topics
Concepts
Viral load
Function (biology)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Coronavirus disease 2019 (COVID-19)
Environmental science
Infection rate
Statistics
Statistical physics
Biological system
Biology
Computer science
Physics
Virology
Mathematics
Genetics
Medicine
Pathology
Disease
Human immunodeficiency virus (HIV)
Surgery
Infectious disease (medical specialty)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.indenv.2024.100025
- OA Status
- diamond
- Cited By
- 6
- References
- 84
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399882657Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.indenv.2024.100025Digital Object Identifier
- Title
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Quantifying quanta: Determining emission rates from clinical dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-21Full publication date if available
- Authors
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Benjamin Jones, Christopher Iddon, Max H. ShermanList of authors in order
- Landing page
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https://doi.org/10.1016/j.indenv.2024.100025Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.indenv.2024.100025Direct OA link when available
- Concepts
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Viral load, Function (biology), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19), Environmental science, Infection rate, Statistics, Statistical physics, Biological system, Biology, Computer science, Physics, Virology, Mathematics, Genetics, Medicine, Pathology, Disease, Human immunodeficiency virus (HIV), Surgery, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_year | 2024 |
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