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：诊所测试在遏制COVID-19等传染病方面发挥着关键作用。然而，在抗击此类大流行病的研究中，关键问题之一是如何在诊所之间优化测试能力。特别是，领域专家希望准确了解如何调整可能影响测试能力的特征，鉴于动态性和不确定性，这是一个极具挑战性的问题。因此，为支持决策者和临床医生，我们与领域专家合作构建了ClinicLens，这是一个交互式可视分析系统，用于探索和优化诊所的测试能力。 ClinicLens拥有基于聚合COVID-19数据的各种功能。它包括后端引擎和前端可视化，带领用户通过提取、训练和预测测试敏感特征和可视化呈现的迭代探索链。它还结合了AI4VIS和可视化分析，展示了诊所如何在考虑一系列特征的影响下优化其测试能力。三个定性案例研究以及来自领域专家的反馈证实，ClinicLens既是一种有用又有效的工具，可用于探索COVID-19的趋势并在地区之间优化诊所的测试能力。整个方法已在网上开源：https： //github.com /Yu Dong5018/clinic-lens。