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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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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

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Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for infectious disease and Endemic disease Control Beijing Center for disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research *** and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza *** compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction ***:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction *** the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is ***:Our research indicates a broad application prospect of deep learning in the field of infectious disease *** characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature ***:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： Graph convolution model infectious disease prediction Multiple age group Multivariate time series Public health

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