Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies

Hamed Bin Furkan; Nabila Ayman; Md Jamal Uddin

doi:10.1016/j.mex.2024.103093

Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies

MethodsX. 2024 Dec 9:14:103093. doi: 10.1016/j.mex.2024.103093. eCollection 2025 Jun.

Authors

Hamed Bin Furkan¹, Nabila Ayman², Md Jamal Uddin^{1

3}

Affiliations

¹ Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
² Department of Computer Science & Engineering, University of Chittagong, Chittagong, Bangladesh.
³ Department of General Educational and Development, Daffodil International University, Dhaka 1216, Bangladesh.

Abstract

In infectious disease outbreak modeling, there remains a gap in addressing spatiotemporal challenges present in established models. This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure. The models' predictions were compared using MAPE, and RMSE, as well as graphical representations generated by employed models. The SARIMA-LSTM model excelled in achieving the lowest average RMSE score of 66.93 as well as reporting the lowest RMSE score for three out of eight countries studied. In this case also, GA-ConvLSTM-CNN model comes in second place with an average RMSE score of 68.46. Considering these results and the ability to follow the seasonal trends of the actual values, this study suggests the SARIMA-LSTM model to be more robust to spatiotemporal challenges compared with the other models under examination. This study•Evaluated established methods with unique imposed difficulty.•Addressed spatiotemporal characteristics of the data.•Proposed the SARIMA-LSTM model based on evaluation metrics.

Keywords: Disease forecast; Disease outbreak prediction; Hybrid Neural Networks; Hybrid neural network; Influenza outbreaks; Neural network comparison; Time series prediction.