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


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
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

Références

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Auteurs

Bruce Mellado (B)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.
iThemba LABS, National Research Foundation, Old Faure Road, Faure 7129, South Africa.

Jianhong Wu (J)

Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada.

Jude Dzevela Kong (JD)

Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada.

Nicola Luigi Bragazzi (NL)

Centre for Disease Modelling, York University, Toronto, ON M3J 1P3, Canada.

Ali Asgary (A)

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 (M)

Gauteng Department of Health, Johannesburg 2107, South Africa.

Nalamotse Choma (N)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.

Kentaro Hayasi (K)

School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2050, South Africa.

Benjamin Lieberman (B)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.

Thuso Mathaha (T)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.

Mduduzi Mbada (M)

Head of Policy at Gauteng Office of the Premier, Johannesburg 2107, South Africa.

Xifeng Ruan (X)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.

Finn Stevenson (F)

School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa.

James Orbinski (J)

Dahdaleh Institute for Global Health Research, York University, Toronto, ON M3J 1P3, Canada.

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Classifications MeSH