Abstract
The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and—through data visiting, across facilities—for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.
| Original language | English |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Advanced Genetics |
| Volume | 2 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 11 Jun 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Data Science
- Ethical
- Health Information
- Knowledge Capture
- Legal and Social Implications
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