Risk EvaLuation fAst iNtelligent Tool (RELIANT)
- Funded by UK Research and Innovation (UKRI)
- Total publications:8 publications
Grant number: EP/V036777/1
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Key facts
Disease
COVID-19Start & end year
20202022Known Financial Commitments (USD)
$1,731,627.63Funder
UK Research and Innovation (UKRI)Principal Investigator
PendingResearch Location
United KingdomLead Research Institution
University of GlasgowResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Environmental stability of pathogen
Special Interest Tags
Data Management and Data SharingInnovation
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
This project brings together unique expertise in Computational and Experimental Fluid Dynamics, Model Reduction and Artificial Intelligence, to identify solutions for the management of people and spaces in the current pandemic and post lockdown. A new interactive tool is proposed that evaluates the risk of infection in the indoor environment from droplets and aerosols generated when breathing, talking, coughing and sneezing. This capability will become more critical as winter approaches and building ventilation will need to be limited for comfort considerations. The fluid dynamic behaviour of droplets and aerosols, the effect of using face masks as well as other parameters such as room volume, ventilation and number of occupants are considered. A datahub capable of storing, curating and managing heterogeneous data from sources internal and external to the project will be created. A synergetic experimental and numerical approach will be undertaken. These will complement the existing literature and data from other EPSRC-funded projects providing suitable datasets with adequate resolution in time and space for all the relevant features. To support experiments and numerical simulations, reduced order models capable of interpolating and extrapolating the scenarios collected in the database will be used. This will permit the estimation of droplet and aerosol concentrations and distributions in unknown scenarios at low-computational cost, in near realtime. A state-of-the-art AI-based framework, incorporating descriptive, predictive and prescriptive techniques will extract the knowledge from the data and drive the decision-making process and provide in near real-time the assessment of risk levels.
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