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
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.
Int J Environ Res Public Health. 2021 Jul 26;18(15):7890. doi: 10.3390/ijerph18157890.
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.
新冠疫情正在造成巨大的健康、社会和经济代价。虽然许多发达国家已开始接种疫苗,但大多数非洲国家仍在等待疫苗分配,并采用临床公共卫生(CPH)策略来控制疫情。值得关注的变异毒株(VOC)的出现、疫苗供应的获取不平等以及当地特定的物流和疫苗配送参数,给各国的CPH策略增添了复杂性,并加剧了对有效CPH政策的迫切需求。大数据、人工智能机器学习技术及合作有助于对多个数据源进行准确、及时、符合当地情况的细致分析,为CPH决策、疫苗接种策略及其分阶段推出提供依据。非洲-加拿大人工智能与数据创新联盟(ACADIC)已成立,旨在开发并应用机器学习技术来设计非洲的CPH策略,这需要持续的合作、测试和开发,以最大限度地提高与新冠疫情相关的CPH干预措施的公平性和有效性。