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

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    Foreign, Commonwealth & Development Office (FCDO), Wellcome Trust
  • Principle Investigator

  • Research Location

    Uganda, Africa
  • Lead Research Institution

    MRC/UVRI and LSHTM Uganda Research Unit
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory


  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment


  • Age Group


  • Vulnerable Population


  • Occupations of Interest



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.