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Research and Analysis of Epidemic Prevention and Control Based on BP Neural Network

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DOI: 10.23977/jnca.2022.070106 | Downloads: 18 | Views: 780

Author(s)

Zheng Zipei 1, Zhu Xintao 2, Li Ningxin 3, Liu Zichang 1

Affiliation(s)

1 School of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin, Hexi, 300222, China
2 School of International Economics and Trade, Ningbo University of Finance & Economics, Ningbo, Zhejiang, 315000, China
3 School of Mathematics, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China

Corresponding Author

Zheng Zipei

ABSTRACT

The continuous spread of COVID-19, especially the emergence and spread of a series of mutant strains, has brought new challenges to the development of human society, and the emergence of sewage epidemiology has provided a new idea for the early warning and detection of COVID-19 in countries around the world. By analyzing the rationality and optimal site selection of sewage sampling sites in the United States, early warning of the local epidemic development trend was conducted, and corresponding measures were proposed for epidemic prevention and control by the local government. The BP neural network prediction method is adopted to carry out deep learning and prediction of the changes in the number of infected persons in the next 5 weeks in each state of the United States based on the data related to the number of infected persons in the recent 18 weeks in the United States with 70% training ratio, 15% verification ratio and 15% rationality ratio. In addition, the epidemic development trend of each state in the United States is visualized in the form of the top ten regions in the United States. The threshold of epidemic development in each state of the United States was calculated by using SIR epidemic model, and the prediction results of BP neural network model were used to obtain the areas likely to break out large-scale epidemic, and based on the results, prevention and control suggestions were put forward for the areas likely to break out large-scale epidemic.

KEYWORDS

Grey relational analysis, TOPSIS method, BP neural network, SIR infectious disease model

CITE THIS PAPER

Zheng Zipei, Zhu Xintao, Li Ningxin, Liu Zichang, Research and Analysis of Epidemic Prevention and Control Based on BP Neural Network. Journal of Network Computing and Applications (2022) Vol. 4: 29-32. DOI: http://dx.doi.org/10.23977/jnca.2022.070106.

REFERENCES

[1] Liu Sifeng. Grey System Theory and Its Application (5th edition) [M]. Beijing: Science Press, 2010:62.
[2] Yang Shue, Huang Li. System engineering-theory & practice,2005,25(1):12-18,26.
[3] Liu Y. Research on epidemic and vaccine efficacy model based on basic regeneration rate [D]. Sichuan: University of Electronic Science and Technology of China,2010.
[4] Li Changkun, Wang Xiuhai. Research on supply chain knowledge sharing risk early warning system based on BP neural network [J]. Science and Technology Management Research, 2009,29(4):191-193.
[5] Li Ruiqi, Wang Wei, Shu Panpan, Yang Hui, Pan Liming, Cui Aixiang, Tang Ming. A review of outbreak threshold analysis of epidemic transmission dynamics on complex networks [J]. Complex Systems and Complexity Science, 2016,13 (01):1-39.

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