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AI for science: Predicting infectious diseases

Journal of Safety Science and Resilience 2024年第2期5卷 130-146页

作者： Alexis Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Jiaojiao Wang Yue Xiang Da Xie Xi Lu Institute of Automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBejing 100090China Systems Engineering Cornll UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Collge of Electrical Engineering Sichuan UniversityChina Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Electrical Engineering Southeast UniversityNanjing 210096China

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious *** epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease ***,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive *** is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease *** paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological *** harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced *** comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by *** essence,Al4S represents a paradigm shift in infectious disease *** addresses the limitations of traditional models and paves the way for a more proactive and informed response to future *** we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

关键词： AI for science(AI4S) Data integration global healthchallenges Infectious disease prediction Predictive modeling Real-timemonitoring

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