African COVID-19 Preparedness (AFRICO19)

Grant number: 220977/Z/20/Z

Grant search

Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2023
  • Known Financial Commitments (USD)

    $824,011.54
  • Funder

    Wellcome Trust
  • Principal Investigator

    Prof Matthew Louis Cotten
  • Research Location

    United Kingdom
  • Lead Research Institution

    London School of Hygiene & Tropical Medicine
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • 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.

Publicationslinked via Europe PMC

Last Updated:38 minutes ago

View all publications at Europe PMC

Generating realistic single-cell images from CellProfiler representations.

From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences

Mutational signature dynamics indicate SARS-CoV-2's evolutionary capacity is driven by host antiviral molecules.

SARS-CoV-2’s evolutionary capacity is mostly driven by host antiviral molecules

SARS-CoV-2 Omicron BA.5 Infections in Vaccinated Persons, Rural Uganda.

Near-Complete Genome Sequences of Measles Virus Strains from 10 Years of Uganda Country-wide Surveillance.

Establishing farm dust as a useful viral metagenomic surveillance matrix.

Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study.