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| Funder | European Commission |
|---|---|
| Recipient Organization | University College London |
| Country | United Kingdom |
| Start Date | May 01, 2022 |
| End Date | Apr 30, 2025 |
| Duration | 1,095 days |
| Number of Grantees | 24 |
| Roles | Participant; Third Party; Coordinator |
| Data Source | European Commission |
| Grant ID | 101046314 |
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.
We will 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.
We will inform future preparedness and response 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 an artificial intelligence driven tool for the prediction of long COVID.
Laboratoire National de Sante; Fiotec-Fundacao Para O Desenvolvimento Cientifico E Tecnologico Em Saude; Folkehelseinstituttet; Dopasi Foundation; Institut Catala de la Salut; Stichting Amsterdam Institute for Global Health and Development; Universite de Geneve; Fundacao Manhica; Fundacio Institut Universitari Pera la Recerca A L'Atencio Primaria de Salut Jordi Gol I Gurina; Fundacion Privada Instituto de Salud Global Barcelona; Fondazione Irccs Ca' Granda - Ospedale Maggiore Policlinico; Institut de Investigacio En Ciencies de la Salut Germans Trias I Pujol; Fundacao Oswaldo Cruz; Nigeria Centre for Disease Control and Prevention; African Field Epidemiology Network Lbg; Universitatsklinikum Heidelberg; Universita Degli Studi Di Milano; Hospital Clinic de Barcelona; University of the Philippines System; Public Health Foundation of India; Instituto de Medicina Molecular Joao Lobo Antunes; University College London; Arab American University; Drugs for Neglected Diseases Initiative Fondation
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