Completed Genetics & Molecular Biology Infection & Immunity

African COVID-19 Preparedness (AFRICO19)

In plain English

AI plain-English summary

African scientists are deploying portable DNA sequencers and machine learning tools to track SARS-CoV-2 as it spreads across the continent. The AFRICO19 consortium links three African research sites—in East and West Africa—with genomics support from the University of Glasgow to build local capacity for rapid viral diagnosis, genome sequencing, and contact tracing. Most existing COVID-19 surveillance infrastructure and diagnostic tools were developed outside Africa, leaving the continent reliant on external labs and slow turnaround times. This project fills that gap by establishing on-the-ground sequencing networks, deploying novel diagnostics optimised for SARS-CoV-2, and using machine learning to refine an African-specific case definition for the disease. Researchers will also monitor viral evolution in established patient cohorts, tracking how the virus mutates as it moves through communities. If successful, the project could transform outbreak response across the region: faster local sequencing means public health teams can identify new variants, trace transmission chains, and implement quarantine measures in days rather than weeks. The real-time analysis platform, CoV-GLUE, will share findings openly with global researchers, strengthening pandemic preparedness far beyond Africa.

View original technical description
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.

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Researchers

David Robertson (EPMC Awardee)James Nokes (EPMC Awardee)Ke Yuan (EPMC Awardee)Martin Antonio (EPMC Awardee)Matthew Cotten (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Expansion and support of SARS-CoV-2 sequencing in West and Central Africa to support the COVID-19 pandemic response
Leveraging COG-UK expertise to support the global dissemination of SARS-CoV-2 genome sequencing
Cross-AAPs acceleration of genomics for escalating infectious diseases
NIHR Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance, University of Oxford
The AfricAsia Single Cell Genomics Initiative

Original classification

Coronavirus

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