African COVID-19 Preparedness (AFRICO19)
- Funded by Foreign, Commonwealth & Development Office (FCDO), Wellcome Trust
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202022Known Financial Commitments (USD)
$2,462,447.7Funder
Foreign, Commonwealth & Development Office (FCDO), Wellcome TrustPrincipal Investigator
PendingResearch Location
N/ALead Research Institution
MRC/UVRI and LSHTM Uganda Research UnitResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Diagnostics
Special Interest Tags
Data Management and Data Sharing
Study Type
Clinical
Clinical Trial Details
Not applicable
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
Our project, AFRICO19, will enhance capacity to understand SARS-CoV-2/hCoV-19 infection in three regions of Africa and globally. Building on existing infrastructures and collaborations we will create a network to share knowledge on next generation sequencing (NGS), including Oxford Nanopore Technology (MinION), coronavirus biology and COVID-19 disease control. Our consortium links three African sites combined with genomics and informatics support from the University of Glasgow to achieve the following key goals: 1. Support East and West African capacities for rapid diagnosis and sequencing of SARS-CoV-2 to help with contact tracing and quarantine measures. Novel diagnostic tools optimized for this virus will be deployed. An African COVID-19 case definition will be refined using machine learning for identification of SARS-CoV-2 infections. 2. Surveillance of SARS-CoV-2 will be performed in one cohort at each African site. This will use established cohorts to ensure that sampling begins quickly. A sampling plan optimized to detect initial moderate and severe cases followed by household contact tracing will be employed to obtain both mild to severe COVID-19 cases. 3. Provide improved understanding of SARS-CoV-2 biology/evolution using machine learning and novel bioinformatics analyses. Our results will be shared via a real-time analysis platform using the newly developed CoV-GLUE resource.