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

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    UK Research and Innovation (UKRI)
  • Principle Investigator

  • Research Location

    United Kingdom, Europe
  • Lead Research Institution

    University of Glasgow
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Environmental stability of pathogen

  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable


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.

Publicationslinked via Europe PMC

Last Updated:38 minutes ago

View all publications at Europe PMC

Real-time updating of dynamic social networks for COVID-19 vaccination strategies.

Chloroplast genome skimming of a potential agroforestry species Melia dubia. Cav and its comparative phylogenetic analysis with major Meliaceae members.

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.

Enhancing high-fidelity nonlinear solver with reduced order model.

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport - A machine learning approach.

Numerical study of COVID-19 spatial-temporal spreading in London.

Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach.