A Deep Learning Model for Mobility Change Prediction Based on National Prevention and Control Policy

The prediction of mobility change is a crucial aspect of infectious disease prevention and control, as individual movement can lead to the spread of infectious diseases. After an epidemic outbreak, countries usually implement a series of epidemic prevention and control policies to restrict personnel travel. This paper aims to measure the impact of national epidemic prevention and control policies on mobility change. To achieve this goal, we propose a mobility change prediction model based on LSTM and "local" fully connected layers. The results show that the model’s mobility change predictions in the six mobility categories are consistent with reality, and the model is optimal overall.