Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncertainty make this a highly challenging problem. Hence, as a tool to support both policymakers and clinicians, we collaborated with domain experts to build ClinicLens, an interactive visual analytics system for exploring and optimizing the testing capacities of clinics. ClinicLens houses a range of features based on an aggregated set of COVID-19 data. It comprises Back-end Engine and Front-end Visualization that take users through an iterative exploration chain of extracting, training, and predicting testing-sensitive features and visual representations. It also combines AI4VIS and visual analytics to demonstrate how a clinic might optimize its testing capacity given the impacts of a range of features. Three qualitative case studies along with feedback from subject-matter experts validate that ClinicLens is both a useful and effective tool for exploring the trends in COVID-19 and optimizing clinic testing capacities across regions. The entire approach has been open-sourced online: https://github.com/YuDong5018/clinic-lens.

翻译：诊所检测在控制COVID-19等传染性疾病中发挥着关键作用。然而，应对此类疫情的核心研究问题之一是如何优化各诊所的检测能力。特别地，领域专家期望确切了解如何调整可能影响检测能力的特征，而动态性与不确定性使得这一问题极具挑战性。为此，作为支持政策制定者与临床医生的工具，我们与领域专家合作构建了ClinicLens——一个交互式可视化分析系统，用于探索和优化诊所检测能力。ClinicLens基于聚合后的COVID-19数据集提供一系列特征分析功能。该系统由后端引擎和前端可视化组成，引导用户经历提取、训练和预测检测敏感特征及其可视化表征的迭代探索链条。它还融合了AI4VIS与可视化分析，展示在多重特征影响下诊所如何优化其检测能力。三项定性案例研究及领域专家反馈验证了ClinicLens在探索COVID-19趋势与跨区域优化诊所检测能力方面兼具实用性与有效性。整套方法已开源发布于：https://github.com/YuDong5018/clinic-lens。