Document Type
Oral/Panel
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Publication Date
4-2021
Abstract
A pandemic like COVID-19 occurred on an exponential scale where nations all over the globe surrendered to its contagious outcome and went into lockdown conditions. The prediction of this epidemic is of vital importance, to understand its consequences, possible threats, predict casualties, and decide one's actions so precautions can be ensured. In our study, we employ machine-learning models that are built on statistical techniques to determine COVID's impact, spread rate, and recovery rates on both worldwide and country-wise data (publicly available). Considering unprecedented factors and to avoid possible deviation from the actual values, our data-driven model can provide short-term predictions. We also provide interactive visualization techniques, such as Heatmaps and Waffle plots to better understand the coronavirus spread graphically.
First Advisor
KC Santosh
Research Area
Computer Science
Recommended Citation
Khurana, Shaurya, "COVID-19: Prediction and visualization" (2021). IdeaFest. 418.
https://red.library.usd.edu/idea/418