Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa.
Africa
COVID-19
artificial intelligence
big data
vaccine roll-out
Journal
International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455
Informations de publication
Date de publication:
26 07 2021
26 07 2021
Historique:
received:
06
07
2021
accepted:
13
07
2021
entrez:
7
8
2021
pubmed:
8
8
2021
medline:
12
8
2021
Statut:
epublish
Résumé
COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.
Identifiants
pubmed: 34360183
pii: ijerph18157890
doi: 10.3390/ijerph18157890
pmc: PMC8345600
pii:
doi:
Types de publication
Editorial
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
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