Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 21, 20 December 2023
Open
Access | Article
Forecasting the number of new crown infections in China based on machine learning methods
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Abstract
Based on the current state and the evolution of the new crown epidemic in China, this pa-per uses machine learning models and historical data to predict the changes in the number of new crown infections in China in the next four months. First, analyzing the background and current situation of the new crown epidemic, and identified the research question by collecting relevant historical data, including indicators such as the number of infected people, the number of cured people, and the number of deaths. Second, employing ma-chine learning models and MIR model to predict the trend and scale of the number of new crown infections in China over the next four months. Finally, coming to a forecast conclu-sion: in the next four months, the number of new crown infections in China will drop very slightly (almost remain unchanged) every month, and the monthly infection rate will re-main at a low level. At the same time, discussing and summarizing the application value of the conclusions. The research results of this paper can provide useful references and guidance for government policymakers and the public, helping them better deal with the epidemic and formulate corresponding measures. In addition, the research methods and models in this paper also have a certain degree of versatility, which can provide a certain reference for other countries and regions to predict the trend and scale of the new crown epidemic.
Keywords
prediction, infections, model
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Biological Engineering and Medical Science
- ISBN (Print)
- 978-1-83558-215-2
- ISBN (Online)
- 978-1-83558-216-9
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/21/20230799
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited