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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8345600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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