MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting Article Swipe
Thắng Nguyễn
,
Dung Nguyen
,
Pham Tien Kha
,
Truyen Tran
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.06781
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.06781
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.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.06781
- https://arxiv.org/pdf/2411.06781
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4404391431Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.06781Digital Object Identifier
- Title
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MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic ForecastingWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-11Full publication date if available
- Authors
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Thắng Nguyễn, Dung Nguyen, Pham Tien Kha, Truyen TranList of authors in order
- Landing page
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https://arxiv.org/abs/2411.06781Publisher landing page
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https://arxiv.org/pdf/2411.06781Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2411.06781Direct OA link when available
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Artificial neural network, Phase (matter), Computer science, Artificial intelligence, Operations research, Statistical physics, Physics, Engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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