Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

• Main Authors: Bruce Mellado, Jianhong Wu, Jude Dzevela Kong, Nicola Luigi Bragazzi, Ali Asgary, Mary Kawonga, Nalamotse Choma, Kentaro Hayasi, Benjamin Lieberman, Thuso Mathaha, Mduduzi Mbada, Xifeng Ruan, Finn Stevenson, James Orbinski
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: COVID-19; Africa; vaccine roll-out; big data; artificial intelligence
• Online Access: https://www.mdpi.com/1660-4601/18/15/7890
• ISSN: 1661-7827; 1660-4601

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.