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

ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty