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

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 con...

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Autores principales: Mellado, Bruce, Wu, Jianhong, Kong, Jude Dzevela, Bragazzi, Nicola Luigi, Asgary, Ali, Kawonga, Mary, Choma, Nalamotse, Hayasi, Kentaro, Lieberman, Benjamin, Mathaha, Thuso, Mbada, Mduduzi, Ruan, Xifeng, Stevenson, Finn, Orbinski, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345600/
https://www.ncbi.nlm.nih.gov/pubmed/34360183
http://dx.doi.org/10.3390/ijerph18157890
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author Mellado, Bruce
Wu, Jianhong
Kong, Jude Dzevela
Bragazzi, Nicola Luigi
Asgary, Ali
Kawonga, Mary
Choma, Nalamotse
Hayasi, Kentaro
Lieberman, Benjamin
Mathaha, Thuso
Mbada, Mduduzi
Ruan, Xifeng
Stevenson, Finn
Orbinski, James
author_facet Mellado, Bruce
Wu, Jianhong
Kong, Jude Dzevela
Bragazzi, Nicola Luigi
Asgary, Ali
Kawonga, Mary
Choma, Nalamotse
Hayasi, Kentaro
Lieberman, Benjamin
Mathaha, Thuso
Mbada, Mduduzi
Ruan, Xifeng
Stevenson, Finn
Orbinski, James
author_sort Mellado, Bruce
collection PubMed
description 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.
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spelling pubmed-83456002021-08-07 Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa Mellado, Bruce Wu, Jianhong Kong, Jude Dzevela Bragazzi, Nicola Luigi Asgary, Ali Kawonga, Mary Choma, Nalamotse Hayasi, Kentaro Lieberman, Benjamin Mathaha, Thuso Mbada, Mduduzi Ruan, Xifeng Stevenson, Finn Orbinski, James Int J Environ Res Public Health Editorial 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. MDPI 2021-07-26 /pmc/articles/PMC8345600/ /pubmed/34360183 http://dx.doi.org/10.3390/ijerph18157890 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Editorial
Mellado, Bruce
Wu, Jianhong
Kong, Jude Dzevela
Bragazzi, Nicola Luigi
Asgary, Ali
Kawonga, Mary
Choma, Nalamotse
Hayasi, Kentaro
Lieberman, Benjamin
Mathaha, Thuso
Mbada, Mduduzi
Ruan, Xifeng
Stevenson, Finn
Orbinski, James
Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title_full Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title_fullStr Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title_full_unstemmed Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title_short Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
title_sort leveraging artificial intelligence and big data to optimize covid-19 clinical public health and vaccination roll-out strategies in africa
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345600/
https://www.ncbi.nlm.nih.gov/pubmed/34360183
http://dx.doi.org/10.3390/ijerph18157890
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