A COVID-19 Prediction Model Based on Neural Network
- Resource Type
- Conference
- Authors
- Zhang, Xuling; Yan, Hong; Zhang, Zhen; Zhang, Jing; Zhang, Rui; Li, Fuxue
- Source
- 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) Advances in Electrical Engineering and Computer Applications (AEECA), 2021 IEEE International Conference on. :80-84 Aug, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
COVID-19
Training
Epidemics
Analytical models
Recurrent neural networks
Neural networks
Time series analysis
COVID-19 neural network
machine Learning
- Language
The emergence and spread of COVID-19 has had a huge negative impact on society. The use of machine learning methods and big data technology to study the spread and development of the epidemic is a hot topic for many scholars. This paper proposes a model based on the $\text{LSTM}+\text{BPNN}$ neural network, which predicts the development trend of the new crown epidemic through the migration data of urban population flows, and proves the effectiveness of the model through a large number of experiments.