Journal article
C. G. Healey, S. J. Simmons, C. Manivannan, Y. Ro
Big Data, vol. 10(2), 2022, pp. 95-114
Big Data, vol. 10(2), 2022, pp. 95-114
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Healey, C. G., Simmons, S. J., Manivannan, C., & Ro, Y. (2022). Visual analytics for the coronavirus COVID-19 pandemic. Big Data, 10(2), 95–114.
Chicago/Turabian
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Healey, C. G., S. J. Simmons, C. Manivannan, and Y. Ro. “Visual Analytics for the Coronavirus COVID-19 Pandemic.” Big Data 10, no. 2 (2022): 95–114.
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Healey, C. G., et al. “Visual Analytics for the Coronavirus COVID-19 Pandemic.” Big Data, vol. 10, no. 2, 2022, pp. 95–114.
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@article{c2022a,
title = {Visual analytics for the coronavirus COVID-19 pandemic},
year = {2022},
issue = {2},
journal = {Big Data},
pages = {95-114},
volume = {10},
author = {Healey, C. G. and Simmons, S. J. and Manivannan, C. and Ro, Y.}
}
Abstract
The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard.† The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.
