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.
目的以一项全膝关节置换术多中心随机对照临床研究为例,介绍利用REDCap(Research Electronic Data Capture)系统导入及分析数据的方法。方法利用REDCap系统的多功能工具,包括数据导入工具、数据导出、报表与统计功能、项目仪表盘及编码手册等,处理和分析全膝关节置换术多中心随机对照临床研究数据。电子化的临床数据经过调整与标准化后,通过REDCap的数据导入工具批量上传至系统。使用REDCap的数据导出功能对数据进行初步整理,并借助其统计与报表功能进行描述性统计分析,以确保数据质量和完整性。结果通过REDCap系统成功创建了膝骨关节炎临床研究的电子数据采集与管理平台。该平台能够实时采集来自多个中心的临床数据,并通过内置的数据管理和质量控制机制,确保数据的准确性和一致性。借助REDCap的统计分析功能,研究团队能够实时监测数据并进行质量评估和动态分析,为后续的深入统计分析提供了基础。结论利用REDCap系统可以搭建全新的临床研究项目,开展中的临床研究也可利用其调整、导入和分析电子化数据,从而提高数据管理的科学性和研究效率。
