Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning fr...Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.展开更多
Let X=Σ_(i=1)^(n)a_(i)ξ_(i)be a Rademacher sum with Var(X)=1 and Z be a standard normal random variable.This paper concerns the upper bound of|P(X≤x)−P(Z≤x)|for any x∈R.Using the symmetric properties and R softwa...Let X=Σ_(i=1)^(n)a_(i)ξ_(i)be a Rademacher sum with Var(X)=1 and Z be a standard normal random variable.This paper concerns the upper bound of|P(X≤x)−P(Z≤x)|for any x∈R.Using the symmetric properties and R software,this paper gets the following improved Berry-Esseen type bound under some conditions,|P(X≤x)−P(Z≤x)|≤P(Z∈(0,a1)),∀x∈R,which is one of the modified conjecture proposed by Nathan K.and Ohad K.展开更多
基金supported by the National Natural Science Foundation of China(32370703)the CAMS Innovation Fund for Medical Sciences(CIFMS)(2022-I2M-1-021,2021-I2M-1-061)the Major Project of Guangzhou National Labora-tory(GZNL2024A01015).
文摘Viral infectious diseases,characterized by their intricate nature and wide-ranging diversity,pose substantial challenges in the domain of data management.The vast volume of data generated by these diseases,spanning from the molecular mechanisms within cells to large-scale epidemiological patterns,has surpassed the capabilities of traditional analytical methods.In the era of artificial intelligence(AI)and big data,there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information.Despite the rapid accumulation of data associated with viral infections,the lack of a comprehensive framework for integrating,selecting,and analyzing these datasets has left numerous researchers uncertain about which data to select,how to access it,and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels,from the molecular details of pathogens to broad epidemiological trends.The scope extends from the micro-scale to the macro-scale,encompassing pathogens,hosts,and vectors.In addition to data summarization,this review thoroughly investigates various dataset sources.It also traces the historical evolution of data collection in the field of viral infectious diseases,highlighting the progress achieved over time.Simultaneously,it evaluates the current limitations that impede data utilization.Furthermore,we propose strategies to surmount these challenges,focusing on the development and application of advanced computational techniques,AI-driven models,and enhanced data integration practices.By providing a comprehensive synthesis of existing knowledge,this review is designed to guide future research and contribute to more informed approaches in the surveillance,prevention,and control of viral infectious diseases,particularly within the context of the expanding big-data landscape.
基金supported by the National Natural Science Foundation of China(Grant No.11861029)the Hainan Provincial Natural Science Foundation of China(Grants Nos.122MS056,124MS056).
文摘Let X=Σ_(i=1)^(n)a_(i)ξ_(i)be a Rademacher sum with Var(X)=1 and Z be a standard normal random variable.This paper concerns the upper bound of|P(X≤x)−P(Z≤x)|for any x∈R.Using the symmetric properties and R software,this paper gets the following improved Berry-Esseen type bound under some conditions,|P(X≤x)−P(Z≤x)|≤P(Z∈(0,a1)),∀x∈R,which is one of the modified conjecture proposed by Nathan K.and Ohad K.
