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• W4308309964 endingPage "43" @default.
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• W4308309964 abstract "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." @default.
• W4308309964 created "2022-11-10" @default.
• W4308309964 creator A5022511196 @default.
• W4308309964 creator A5038838057 @default.
• W4308309964 creator A5067414355 @default.
• W4308309964 creator A5086591805 @default.
• W4308309964 date "2023-02-01" @default.
• W4308309964 modified "2023-10-17" @default.
• W4308309964 title "Application of big data and artificial intelligence in epidemic surveillance and containment" @default.
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• W4308309964 doi "https://doi.org/10.1016/j.imed.2022.10.003" @default.
• W4308309964 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36373090" @default.
• W4308309964 hasPublicationYear "2023" @default.

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