TY - JOUR
T1 - Incomplete COVID-19 Data
T2 - The curation of medical health data by the virus outbreak data Network-Africa
AU - van Reisen, Mirjam
AU - Oladipo, Francisca Onaolapo
AU - Mpezamihigo, Mouhamed
AU - Plug, Ruduan
AU - Basajja, Mariam
AU - Aktau, Aliya
AU - Jati, Putu Hadi Purnama
AU - Nalugala, Reginald
AU - Folorunso, Sakinat
AU - Amare, Samson Yohannes
AU - Abdulahi, Ibrahim
AU - Afolabi, Oluwole Olumuyiwa
AU - Mwesigwa, Ezra
AU - Taye, Getu Tadele
AU - Kawu, Abdulahi
AU - Ghardallou, Mariem
AU - Liang, Yan
AU - Osigwe, Obinna
AU - Medhanyie, Araya Abrha
AU - Mawere, Munyaradzi
PY - 2022/10/1
Y1 - 2022/10/1
N2 - The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally. This has become particularly clear with the recent emergence of new variants of concern. The Virus Outbreak Data Network (VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care, which renders data production largely meaningless to those producing it. This modus operandi leads to disfranchisement over the control of health data, which is extracted to be processed elsewhere. In response to this problem, VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process, would have a greater chance of being adopted. The design team based their work on the legal requirements of the European Union's General Data Protection Regulation (GDPR); the FAIR Guidelines on curating data as Findable, Accessible (under well-defined conditions), Interoperable and Reusable (FAIR); and national regulations applying in the context where the data is produced. The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data. A condition of such innovation is that the innovation team is intradisciplinary, involving stakeholders and experts from all of the places where the innovation is designed, and employs a methodology of co-creation and capacity-building.
AB - The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally. This has become particularly clear with the recent emergence of new variants of concern. The Virus Outbreak Data Network (VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care, which renders data production largely meaningless to those producing it. This modus operandi leads to disfranchisement over the control of health data, which is extracted to be processed elsewhere. In response to this problem, VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process, would have a greater chance of being adopted. The design team based their work on the legal requirements of the European Union's General Data Protection Regulation (GDPR); the FAIR Guidelines on curating data as Findable, Accessible (under well-defined conditions), Interoperable and Reusable (FAIR); and national regulations applying in the context where the data is produced. The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data. A condition of such innovation is that the innovation team is intradisciplinary, involving stakeholders and experts from all of the places where the innovation is designed, and employs a methodology of co-creation and capacity-building.
KW - FAIR Guidelines
KW - FAIR Equivalency
KW - Federated data
KW - Digital health
KW - GDPR
KW - VODAN-Africa
U2 - 10.1162/dint_e_00166
DO - 10.1162/dint_e_00166
M3 - Editorial
SN - 2096-7004
VL - 4
SP - 673
EP - 697
JO - Data Intelligence
JF - Data Intelligence
IS - 4
ER -