摘要： COVID-19的世界性大流行对整个社会产生了严重的影响，通过数学建模对确诊病例数进行预测将有助于为公共卫生决策提供依据。在复杂多变的外部环境下，基于深度学习的传染病预测模型成为研究热点。然而，现有模型对数据量要求较高，在进行监督学习时不能很好地适应低数据量的场景，导致预测精度降低。构建结合预训练-微调策略的COVID-19预测模型P-GRU。通过在源地区数据集上采用预训练策略，使模型提前获得更多的疫情数据，从而学习到COVID-19的隐式演变规律，为模型预测提供更充分的先验知识，同时使用包含最近历史信息的固定长度序列预测后续时间点的确诊病例数，并在预测过程中考虑本地人为限制政策因素对疫情趋势的影响，实现针对目标地区数据集的精准预测。实验结果表明，预训练策略能够有效提高预测性能，相比于卷积神经网络、循环神经网络、长短期记忆网络和门控循环单元模型，P-GRU模型在平均绝对百分比误差和均方根误差评价指标上表现优异，更适合用于预测COVID-19传播趋势。

关键词: 新型冠状病毒肺炎, 预训练, 微调, 限制政策, 门控循环单元, P-GRU预测模型

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.

Key words: COVID-19, pre-training, fine-tuning, restrictive policy, Gated Recurrent Unit(GRU), P-GRU prediction model

中图分类号: 

• TP391