BACKGROUND
This study focuses on analyzing real data from a hospital to provide timely warnings of known infectious diseases with a view to actively preventing epidemics.
OBJECTIVE
The aim is to design MSRD model to predict the epidemic trend of infectious diseases based on real hospital data.
METHODS
Based on the daily reported data of infectious diseases between 2012–2020 from a large Chinese hospital, we selected seven common infectious diseases and constructed a Multi Self-regression Deep (MSRD) neural network model. This model, which is based on a recurrent neural network, can effectively model the epidemic trend of infectious diseases while considering the current influential factors and characteristics of historical development when calculating time-series data. The mean absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the model’s fit and prediction accuracy.
RESULTS
We compared the MSRD model proposed in this study with the infectious disease SEIR-model using the national public health dataset on COVID-19 and another in-hospital infectious disease, namely, Hand-Foot-and-Mouth disease (HFMD). In an experiment with the national public health dataset, the MSRD proposed in this study demonstrated better performance than the SEIR model, which is because of the SEIR model being limited by factors such as the latent population. The SEIR model is hard to apply to real-world hospital scenarios. Our MSRD model is compared with other neural network methods. The dataset is from real hospital medical records for January 2012–December 2020. The MAE of the MSRD neural network for HFMD and influenza was as low as 0.6928 and 1.3782, respectively. In addition, our MSRD model was compared against other neural network methods such as SVM, Lasso, and Bayes; the MAE and RMSE were both better than those of other neural networks.
CONCLUSIONS
Our MSRD neural network has high prediction accuracy and can predict the development trend of infectious diseases on a daily basis. The MSRD model can act as a hospital infectious-disease early-warning system.