Application of big data and artificial intelligence in epidemic surveillance and containment

大流行 大数据 分离(微生物学) 2019年冠状病毒病(COVID-19) 控制(管理) 公共卫生 医学 计算机安全 业务 风险分析(工程) 计算机科学 医疗急救 人工智能 疾病 护理部 传染病(医学专业) 数据挖掘 病理 生物 微生物学
作者
Zengtao Jiao,Hanran Ji,Jun Yan,Xiaopeng Qi
出处
期刊:Intelligent medicine [Elsevier]
卷期号:3 (1): 36-43 被引量:26
标识
DOI:10.1016/j.imed.2022.10.003
摘要

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.
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