Department of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy.
Department of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy.
Int J Environ Res Public Health. 2021 Apr 23;18(9):4499. doi: 10.3390/ijerph18094499.
Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.
人工智能 (AI) 和机器学习 (ML) 在医学的不同领域的应用已经得到了扩展。在 SARS-CoV-2 爆发期间，AI 和 ML 也被应用于评估和/或实施旨在拉平流行曲线的公共卫生干预措施。本系统评价旨在评估在公共卫生干预措施中应用 AI 和 ML 的效果，以遏制 SARS-CoV-2 的传播。我们的研究结果表明，隔离应该是控制 COVID-19 的最佳策略。全国性封锁也显示出积极的影响，而社会距离隔离只有与其他干预措施（包括关闭学校和商业活动以及限制公共交通）相结合时才被认为是有效的。我们的研究结果还表明，所有干预措施都应在大流行早期开始，并持续一段时间。尽管存在研究局限性，但我们的结论是，AI 和 ML 可以帮助决策者制定控制 COVID-19 大流行的策略。
