Long-term trend prediction of pandemic combining the compartmental and deep learning models.

ABSTRACT: Predicting the spread trends of a pandemic is crucial, but long-term prediction remains challenging due to complex relationships among disease spread stages and preventive policies. To address this issue, we propose a novel approach that utilizes data augmentation techniques, compartmental model features, and disease preventive policies. We also use a breakpoint detection method to divide the disease spread into distinct stages and weight these stages using a self-attention mechanism to account for variations in virus transmission capabilities. Finally, we introduce a long-term spread trend prediction model for infectious diseases based on a bi-directional gated recurrent unit network. To evaluate the effectiveness of our model, we conducted experiments using public datasets, focusing on t

SUBMITTER: Chen W 

PROVIDER: S-EPMC11387753 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature