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END-VOC

ENDing COVID-19 Variants Of concern through Cohort studies (END-VOC)

Logo del proyecto END-VOC
Duración
01/05/2022 - 30/04/2025
Coordinador
Ibrahim Abubakhar (University College of London - UCL)
Financiadores
European Commission, Horizon 2021-2024
Página web
https://endvoc.eu/

The END-VOC consortium will support the European and global response to the COVID-19 pandemic and Variants of Concern (VOC) through well-characterised cohorts and linked with existing European and international initiatives.

END-VOC consists of 19 partners in Europe (UK, Spain, Italy, Germany, Netherlands, Norway, Italy), South America (Brazil and Peru), Africa (Mozambique, South Africa, Nigeria and 13 ANTICOV African countries), Middle East (Palestine) and Asia (India, Pakistan, Philippines) with a focus on countries affected by VOCs and VOIs.

Objective

The project aims to elucidate the global circulation of the current and emerging SARS-CoV-2 VOCs and their characteristics, including transmissibility, pathogenicity and propensity to cause reinfection, to support best control strategies and the development of diagnostics; evaluate the impact of VOCs on the effectiveness of different vaccines and vaccination strategies, and assess the implications of VOCs on the choice of optimal treatment options.

END-VOC will also investigate how VOCs alter long-term post-infection sequelae and where new VOCs emerge within hosts using our clinical cohorts.

The project will inform future preparedness and response by working closely with international and national public health organisations and existing cohort consortia. Specific beyond state-of-the-art components of END-VOC include the use of novel phylogenetic prediction tools and mathematical modelling; generation of powerful cohorts through sentinel surveillance in low and middle-income settings and cohorts of travellers to increase our global reach; use of novel predictive modelling of clinical outcomes by VOC and comorbidity/treatment and evaluation of differences in natural and vaccine immunity by VOC; antiviral screening models within cohorts and artificial intelligence-driven tool for the prediction of long COVID.

Total Funding

9,997,114 €

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