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Details of Grant 

EPSRC Reference: EP/W011956/1
Title: Waves, Lock-Downs, and Vaccines - Decision Support and Model with Superb Geographical and Sociological Resolution
Principal Investigator: Krauss, Professor FM
Other Investigators:
Fong, Professor K Vernon, Dr I Kirkham, Dr T
Researcher Co-Investigators:
Project Partners:
Department: Physics
Organisation: Durham, University of
Scheme: Standard Research
Starts: 01 July 2021 Ends: 31 December 2022 Value (£): 358,057
EPSRC Research Topic Classifications:
Environment & Health Population Ecology
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:  
Summary on Grant Application Form
This proposal aims at further developing, validating, and deploying the JUNE model for the simulation of the spread of COVID-19 in the United Kingdom and the impact of medical and non-medical mitigation strategies. Recently constructed, JUNE combines superb geographical and sociological resolution in its virtual population model with a detailed, flexible and adjustable simulation of daily activities and the ability to include mitigation strategies and other interventions, to asses their effect. JUNE is already being used by NHS England to gain insights into the nature of the disease and the dynamics of its spreading, thereby informing operational planning and supporting decisions.

In this proposal we will

1. continue to provide valuable insights for the NHS, PHE, and the UK government, for example the impact of further complete or partial lock-downs, of the tier system and possible extensions and modifications, and of different vaccination protocols. This includes, in particular, the application of cutting-edge Bayesian methods for uncertainty quantification in projections of possible futures, and the development of robust data assimilation protocols to improve short-term predictions.

2. further refine the population model, with special emphasis on the transmission dynamics and conditions in minorities and on the effect of socio-economic factors on infection and fatality rates. We will also include models for multiple infectious diseases affecting the population concurrently, to quantify the impact of seasonal flu the resulting demands on the health care system.

As a by-product we will fully document the code and produce a convenient user-interface, to facilitate its use in future epidemics, thereby turning it into an enduring national asset.
Key Findings
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