Post by Nadica (She/Her) on Sept 13, 2024 2:03:40 GMT
Long-term trend prediction of pandemic combining the compartmental and deep learning models - Published Sept 9, 2024
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 the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85–4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread.
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 the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85–4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread.