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Editorial
. 2021 Jul 26;18(15):7890.
doi: 10.3390/ijerph18157890.

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

Bruce Mellado  1   2 , Jianhong Wu  3 , Jude Dzevela Kong  3 , Nicola Luigi Bragazzi  3 , Ali Asgary  4 , Mary Kawonga  5 , Nalamotse Choma  1 , Kentaro Hayasi  6 , Benjamin Lieberman  1 , Thuso Mathaha  1 , Mduduzi Mbada  7 , Xifeng Ruan  1 , Finn Stevenson  1 , James Orbinski  8
Affiliations
Editorial

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

Bruce Mellado et al. Int J Environ Res Public Health. .

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: Africa; COVID-19; artificial intelligence; big data; vaccine roll-out.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The receiver operating characteristic curve of the DNN model (see text).
See this image and copyright information in PMC

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