Numerical estimation of new COVID-19 positive cases using time series analysis by machine learning
Authors: 
Yusuke Takamori, Riku Watanabe, Daiju Kato, Masaki Endo, Hiroshi IshikawaAuthors Info & Claims
Yusuke Takamori, Riku Watanabe, Daiju Kato, Masaki Endo, Hiroshi IshikawaAuthors Info & ClaimsPages 153 - 159
Abstract
As described herein, we propose a method to make more accurate predictions based on COVID-19 positive case data from Tokyo, which are provided as open data. Our proposed method uses prediction results of related variables to infer an objective function. Prediction of the number of infected people in Tokyo based on this method yielded better correlation between the predicted results and the actual number of COVID-19 positive cases than prediction of the number of infected people. Results also showed better correlation between prediction results and the actual number of COVID-19 positive cases than prediction based on the number of infected cases alone, indicating that our prediction method provides higher accuracy.
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Information & Contributors
Information
Published In
November 2021
181 pages
ISBN:9781450383141
DOI:10.1145/3444757
- Conference Chairs:
- Richard Chbeir,
- Yannis Manolopoulos,
- Ladjel Bellatreche,
- Djamal Benslimane,
- Program Chairs:
- Mirjana Ivanovic,
- Zakaria Maamar
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Published: 09 November 2021
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MEDES '21
MEDES '21: International Conference on Management of Digital EcoSystems
November 1 - 3, 2021
Virtual Event, Tunisia
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