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: \textit{https://github.com/YuDong5018/clinic-lens

翻译：诊所检测在控制COVID-19等传染病中发挥着关键作用。然而，应对此类大流行病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，领域专家期望精确了解如何调整可能影响检测能力的特征，而动态变化与不确定性使得这一问题极具挑战性。为此，我们与领域专家合作构建了ClinicLens——一个支持政策制定者和临床医生探索与优化诊所检测能力的交互式可视化分析系统。ClinicLens基于聚合的COVID-19数据集，集成了多种特征分析功能。该系统由后端引擎和前端可视化组成，引导用户通过“提取-训练-预测检测敏感特征与可视化表征”的迭代探索链。同时，它结合AI4VIS与可视化分析，展示了在多种特征影响下诊所如何优化其检测能力。三项定性案例研究与领域专家反馈验证了ClinicLens在探索COVID-19趋势及跨区域优化诊所检测能力方面的实用性与有效性。完整方法已开源：\textit{https://github.com/YuDong5018/clinic-lens}