COVID-19 Prediction Model Based on Pre-training and Fine-tuning Strategy

ABSTRACT

The COVID-19 pandemic has had a serious impact on the global society. Building a mathematical model to predict the number of confirmed cases will help provide a basis for public health decision-making.In a complex and changeable external environment, the infectious disease prediction model based on deep learning has become commonly researched. However, the existing models have high requirements regarding the amount of data and cannot adapt to a scene with scarce data during supervised learning. This results in the reduction of model prediction accuracy.The COVID-19 prediction model P-GRU combined with pre-training and fine-tuning strategy is constructed in this study. By adopting the pre-training strategy on the dataset obtained from a specific region, the model is exposed to more epidemic data in advance. Consequently, it can learn the implicit evolution law of COVID-19, provide more sufficient prior knowledge for model prediction, and use the fixed length series containing recent historical information to predict the number of confirmed cases in the future.During the prediction process, the impact of local restrictive policies on the epidemic trend is considered to realize an accurate prediction of the dataset in the target area. The experimental results demonstrate that the pre-training strategy can effectively improve the prediction performance.Compared to Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM) network, and Gated Recurrent Unit (GRU) models, P-GRU model attains excellent performance regarding the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) evaluation indexes. Furthermore, it is more suitable for predicting the transmission trend of COVID-19. © 2022, Editorial Office of Computer Engineering. All rights reserved.