Analysis of an Infectious Disease Vaccination Prediction System Based on the MF-Conv LSTM Model

Ya Wang

doi:10.4108/eetpht.10.7240

Authors

Ya Wang Yongchuan District Center for Disease Prevention and Control

DOI:

https://doi.org/10.4108/eetpht.10.7240

Keywords:

Multi-scale Features, Long short-term Memory Network, Infectious Diseases, Vaccines, Propagation prediction

Abstract

Infectious diseases can seriously threaten people's life safety and have a serious impact on social stability. Therefore, it should improve society’s stability under infectious diseases and ensure the safety of people's lives. A personnel flow feature extraction model based on Multi-Feature Convolutional Long Short-Term Memory (MF-Conv LSTM) is designed based on the characteristics of human daily activity behavior. This can optimize the accuracy of transmission simulation prediction for infectious disease vaccination. When using multi-feature ensemble analysis to extract human daily activity features as input for infectious disease simulation and prediction models, the learner's prediction score for the recurrent infectious diseases reached 0.8705. When using multi-feature ensemble analysis, the predicted scores did not exceed 0.85. The designed infectious disease vaccine transmission prediction model can accurately simulate the infectious behavior of infectious diseases. This provides direction for developing strategies to disrupt the infectious diseases’ spread. This reduces the infectious diseases’ harm to people's personal safety and improves social stability during the spread of large-scale infectious diseases.

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