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 have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the 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, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-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 of 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 against 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.

翻译：可视化建模方法能够实现与丰富数据图形描绘的灵活交互，并支持探索流行病学分析的复杂性。然而，多数流行病学可视化不支持对可能影响传播态势的客观因素进行综合分析，导致缺乏定量与定性证据。为解决这一问题，我们开发了一种名为+msRNAer的基于画像的可视化建模方法。该方法兼顾病毒传播模式的时空特征与社区客观风险因素的多维特征，支持流行病学分析中基于画像的探索与比较。我们将+msRNAer应用于澳大利亚新南威尔士州COVID-19相关聚合数据集，该数据集融合了COVID-19病例数趋势、地理信息、干预事件及基于地方行政区域普查提取的专家监督风险因素。我们通过协同视图完善了+msRNAer工作流程，并通过一项用户研究与三项主题驱动案例研究评估了其可行性、有效性与实用性。专家的积极反馈表明，+msRNAer提供了对分析理解的总体认知，不仅通过画像比较时变情形下病例与风险因素间的关系，还支持在地理基准、时间线及其他因素对比中的导航。通过交互机制，专家发现了长期社区因素在应对疫情脆弱性时潜在模式的功能性与实践性意义。专家确认，+msRNAer有望在其他流行病学分析场景中交付具有时空与多维特征的可视化建模优势。