Prediction for hepatitis trends in Chongqing based on multisource data: a study of delayed input neural network

To construct a time series analysis fusion tool using multisource internet data and then accurately predict the incidence trend of hepatitis in Chongqing.

The incidence rate of hepatitis were obtained from the database of the Centre for Health and Disease Control. Air pollutant data were obtained from the official website of the China Environmental Monitoring Station, climate data were obtained from the National Meteorological Galaxy Center, and network index data were obtained through Baidu search engine. The time duration was from November 2013 to May 2023. Based on existing time series analysis methods, multisource data were used to correct the residual part of the decomposition model. A delayed input neural network (DINN) was constructed based on the respective advantages of non autoregressive (NAR) and long short-term memory (LSTM) recurrent neural networks. Afterwards, optimization modules such as the Nutcracker Optimization Algorithm (NOA) and Joint Quantile Huber Loss (JQHL) were added to the foundation, and then DINN + was constructed.

Compared to common single-input models and synchronous multi-input models, DINN achieved the best prediction performance. After adding hyperparameters and loss function optimization, the predictive performance of DINN+ was further improved, with a mean-square error (MSE) of 0.170 9, a mean absolute error (MAE) of 0.461 2, a root-mean-square error (RMSE) of 0.582 1, a mean absolute percentage error (MAPE) of 0.062 6, and a R-square (R²) of 0.884 0 in a testing set.

Based on the ideas of diverse methods and multidimensional data fusion, we propose a DINN+ optimization model with good accuracy and generalization ability on the basis of previous time series analysis. This model enriches and supplements the methodological research content of using multisource data to calibrate infectious disease time series prediction analysis and can serve as a new benchmark for future analysis of influencing factors and trend prediction of infectious disease public health.