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

Yu Dong; Christy Jie Liang; Yi Chen; Jie Hua

doi:10.1007/s41095-023-0353-5

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Research Article | Open Access

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

Yu Dong^¹, Christy Jie Liang^¹(

), Yi Chen^²(

), Jie Hua^¹

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

Show Author Information

Graphical Abstract

Abstract

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 deve-loped 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.

Keywords

visual modeling epidemiological analysis spatiotemporal multidimensional COVID-19

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Computational Visual Media

Volume 10 Issue 1,
February 2024

Pages 161-186

DOI: 10.1007/s41095-023-0353-5

Cite this article:

Dong Y, Liang CJ, Chen Y, et al. A visual modeling method for spatiotemporal and multidimen-sional features in epidemiological analysis: Applied COVID-19 aggregated datasets. Computational Visual Media, 2024, 10(1): 161-186. https://doi.org/10.1007/s41095-023-0353-5

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Received: 05 December 2022

Accepted: 20 April 2023

Published: 30 November 2023

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