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Proceedings Paper

Prediction model of global COVID-19 based on big data technology
Author(s): Yingbing Fan; Lina Sun; Xuemei Lu; Xianru Bao
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Paper Abstract

In order to overcome the trend influence of novel Coronavirus epidemic in the future, this paper proposes the panel data modeling method based on big data crawler technology, which is based on Python crawler technology to obtain a more effective estimation model from the dynamic perspective of time and cross section. The results showed that the fixed effect error rate established by the development of COVID-19 in China, Japan, South Korea, Germany and Italy was about 3%, and there is a positive correlation between cured cases and confirmed cases of COVID-19. The predicted confirmed cases of COVID-19 in week 63 will be 69, 11,908, 3156, 112293 and 147,545, respectively.

Paper Details

Date Published: 6 May 2022
PDF: 6 pages
Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217606 (6 May 2022); doi: 10.1117/12.2636433
Show Author Affiliations
Yingbing Fan, Heihe University (China)
Lina Sun, College of Science, Heihe University (China)
Xuemei Lu, College of Science, Heihe University (China)
Xianru Bao, College of Science, Heihe University (China)

Published in SPIE Proceedings Vol. 12176:
International Conference on Algorithms, Microchips and Network Applications
Ning Sun; Fengjie Cen, Editor(s)

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