State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
Peking University Third Hospital, Beijing, China.
J Healthc Eng. 2022 Aug 18;2022:8990907. doi: 10.1155/2022/8990907. eCollection 2022.
Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data.
Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error.
The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively.
The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.
传染病通常传播迅速。本研究旨在开发一种模型，该模型可结合疾病传播特征、天气和其他多源数据，使用真实医院数据对传染病进行细粒度的早期预警。
基于 2012 年至 2020 年期间从中国几家大型综合医院收集的传染病每日报告数据，筛选出七种常见的医疗机构传染病，并构建了一个多自回归深度（MSRD）神经网络。该模型采用循环神经网络作为基本结构，通过考虑当前影响条件并同时考虑时间序列数据中的历史发展特征，能够有效地对传染病的流行病学趋势进行建模。使用平均绝对误差（MAE）和均方根误差来评估模型的拟合和预测精度。
与现有的传染病动力学模型（SEIR）相比，所提出的方法具有显著的优势，因为它解决了难以获得定量数据（如潜伏人口、长时间序列的过拟合以及只考虑单一的病人数量序列而不考虑传染病的流行病学特征等问题）。我们还在本研究中比较了某些机器学习方法。实验结果表明，对于手足口病和流感，所提出的方法的 MAE 分别为 0.6928 和 1.3782。
本文提出的基于 MSRD 的传染病预测模型可以为疫情趋势提供每日和即时更新以及准确预测。
