A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets  

作　　者：Yu Dong Christy Jie Liang Yi Chen Jie Hua 

机构地区：[1]School of Computer Science,University of Technology Sydney,Sydney,NSW,2007,Australia [2]Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing,100048,China

出　　处：《Computational Visual Media》2024年第1期161-186,共26页计算可视媒体（英文版）

基　　金：This work is supported by National Natural Science Foundation of China(NSFC)under Grant No.61972010;UTS–CSC Scholarship by the University of Technology Sydney and China Scholarship Council under Agreement No.201908200009.

摘　　要：The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis.However,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative evidence.To address this issue,we developed a portrait-based visual modeling method called+msRNAer.This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological analysis.We applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based censuses.We perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case studies.Positive feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor comparisons.By adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic.Experts confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

关 键 词：visual modeling epidemiological analysis SPATIOTEMPORAL MULTIDIMENSIONAL COVID-19 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]