Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa.
Bruce MelladoJianhong WuJude Dzevela KongNicola Luigi BragazziAli AsgaryMary KawongaNalamotse ChomaKentaro HayasiBenjamin LiebermanThuso MathahaMduduzi MbadaXifeng RuanFinn StevensonJames OrbinskiPublished in: International journal of environmental research and public health (2021)
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.
Keyphrases
- artificial intelligence
- big data
- machine learning
- public health
- coronavirus disease
- sars cov
- deep learning
- healthcare
- decision making
- randomized controlled trial
- mental health
- global health
- physical activity
- copy number
- quality improvement
- climate change
- drinking water
- mass spectrometry
- health information
- data analysis