MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917833520185344 |
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| author | Nguyen, Thang Nguyen, Dung Pham, Kha Tran, Truyen |
| author_facet | Nguyen, Thang Nguyen, Dung Pham, Kha Tran, Truyen |
| contents | Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set of compact differential equations. Data-driven methods such as deep neural networks make no such assumptions and can capture the generative process in more detail, but fail in long-term forecasting due to data limitations. We propose a new hybrid method called MP-PINN (Multi-Phase Physics-Informed Neural Network) to overcome the limitations of these two major approaches. MP-PINN instils the spreading mechanism into a neural network, enabling the mechanism to update in phases over time, reflecting the dynamics of the epidemics due to policy interventions. Experiments on COVID-19 waves demonstrate that MP-PINN achieves superior performance over pure data-driven or model-driven approaches for both short-term and long-term forecasting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_06781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting Nguyen, Thang Nguyen, Dung Pham, Kha Tran, Truyen Artificial Intelligence Machine Learning Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set of compact differential equations. Data-driven methods such as deep neural networks make no such assumptions and can capture the generative process in more detail, but fail in long-term forecasting due to data limitations. We propose a new hybrid method called MP-PINN (Multi-Phase Physics-Informed Neural Network) to overcome the limitations of these two major approaches. MP-PINN instils the spreading mechanism into a neural network, enabling the mechanism to update in phases over time, reflecting the dynamics of the epidemics due to policy interventions. Experiments on COVID-19 waves demonstrate that MP-PINN achieves superior performance over pure data-driven or model-driven approaches for both short-term and long-term forecasting. |
| title | MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.06781 |