| EPSRC Reference: |
EP/V032658/1 |
| Title: |
TRACK: Transport Risk Assessment for COVID Knowledge |
| Principal Investigator: |
Noakes, Professor C |
| Other Investigators: |
| King, Dr M |
Hope, Dr LS |
Pottage, Mr T |
| Amlôt, Professor R |
Linden, Professor PF |
Watling, Professor D |
| Watson, Professor P |
Grant-Muller, Professor S |
James, Professor PM |
| Lopez-Garcia, Dr M |
Bennett, Mr A M |
Moore, Dr G |
| Hall, Professor I |
Apsimon, Professor H |
Pain, Professor CC |
| Blythe, Professor PT |
Shepherd, Professor S |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Civil Engineering |
| Organisation: |
University of Leeds |
| Scheme: |
Standard Research |
| Starts: |
29 September 2020 |
Ends: |
31 December 2023 |
Value (£): |
3,126,526
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| EPSRC Research Topic Classifications: |
| Transport Ops & Management |
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| EPSRC Industrial Sector Classifications: |
| Transport Systems and Vehicles |
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Summary on Grant Application Form |
Public Transport (PT) patronage is currently well below the norm, but as restart progresses the number of people using transport systems will increase. This could increase COVID-19 infection due to increased proximity and interaction with infected persons and contaminated surfaces. TRACK will develop a novel risk model that can simulate infection risk through three transmission mechanisms (droplet, aerosol, surface contact) within different transport vehicles and operating scenarios.
Our interdisciplinary team will collect new data concerning buses, metro and trains (Leeds, Newcastle, London). We will collect air and surface samples to measure SARS-Cov-2 prevalence together with other human biomarkers as a proxy measure for pathogens. We will characterise user and staff travel behaviour and demographics through surveys and passive data collection to relate PT use to geographic and population sub-group disease prevalence. Quantifying proximity of people and their surface contacts through analysis of transport operator CCTV data will enable simulation of micro-behaviour in the transport system. Physical and computational models will be used to evaluate dispersion of infectious droplets and aerosols with different environmental infection control strategies. Data sources will be combined to develop probability distributions for SARS-CoV-2 exposure and simulate transmission risk through a Quantitative Microbial Risk Assessment (QMRA) framework.
Working closely with Department for Transport (DfT) and transport stakeholders, TRACK will provide microbial and user data, targeted guidance and risk planning tools that will directly enable better assessment of infection risks for passengers and staff using surface PT networks, and help policy teams design effective interventions to mitigate transmission
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.leeds.ac.uk |