Semester of Graduation

Fall

Degree

Master of Science in Computer Science (MSCS)

Department

Computer Science and Engineering

Document Type

Thesis

Abstract

The number of COVID-19 cases has been continuously increasing worldwide since the beginning of 2020. A lot of efforts have been made in identifying, predicting, and mitigating this deadly virus infection. This requires gaining insight into large collections of the related data, which are multivariate in nature involving many variables/attributes. Here we develop a parallel coordinates system to visualize the COVID-19 data in an interactive manner considering all variables, including location and time. The challenges with this approach include mapping many variables of different types (parallel axes) and occlusion associated with highly cluttered data lines. We adopt several functionalities to make our parallel coordinates plot useful, such as two- level axes representation, axes reordering and scaling, data filtering, highlighting, and brushing. A detailed visualization of COVID-19 data reveals several interesting features. For instance, hard-hit countries, including USA and India, had gone through 2 or 4 episodes (waves) of virus infection as shown by large daily new cases followed by high daily deaths in two or three weeks. Such temporal trend is not clear in some other countries, including Brazil. Furthermore, correlations between the vaccination and the infection do not consistently imply a positive impact of the vaccination over time. Other factors, such as social interaction, under-vaccination, and adaptive spreading nature of the virus seem to play role in the continued high level of COVID-19 infection worldwide.

Committee Chair

Karki, Bijaya B.

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