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

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 and James Orbinski
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Bruce Mellado: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
Jianhong Wu: Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada
Jude Dzevela Kong: Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada
Nicola Luigi Bragazzi: Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada
Ali Asgary: Disaster & Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada
Mary Kawonga: Gauteng Department of Health, Johannesburg 2107, South Africa
Nalamotse Choma: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
Kentaro Hayasi: School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2050, South Africa
Benjamin Lieberman: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
Thuso Mathaha: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
Mduduzi Mbada: Head of Policy at Gauteng Office of the Premier, Johannesburg 2107, South Africa
Xifeng Ruan: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
Finn Stevenson: School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa
James Orbinski: Dahdaleh Institute for Global Health Research, York University, Toronto, ON M3J 1P3, Canada

IJERPH, 2021, vol. 18, issue 15, 1-7

Abstract: 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.

Keywords: COVID-19; Africa; vaccine roll-out; big data; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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