City Research Online

Abstract

The COVID-19 pandemic has been a period where time-series of disease statistics, such as the number of cases or vaccinations, have been intensively used by public health professionals to estimate how their region compares to others and estimate what future could look like at home. Conventional visualizations are often limited in terms of advanced comparative features and in supporting forecasting systematically. This paper presents a visual analytics approach to support data-driven prediction based on a search-Analyze-predict process comprising a multi-metric, multi-criteria time-series search method and a data-driven prediction technique. These are supported by a visualization framework for the comprehensive comparison of multiple time-series. We inform the design of our approach by getting iterative feedback from public health experts globally, and evaluate it both quantitatively and qualitatively.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Human computer interaction, Pandemics, Visual analytics, Search methods, Data visualization, Vaccines, Iterative methods
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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