Summary of Mp-pinn: a Multi-phase Physics-informed Neural Network For Epidemic Forecasting, by Thang Nguyen et al.
MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
by Thang Nguyen, Dung Nguyen, Kha Pham, Truyen Tran
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MP-PINN (Multi-Phase Physics-Informed Neural Network) method combines the strengths of mechanistic models and data-driven approaches to forecast temporal processes, such as virus spreading in epidemics. By instilling the spreading mechanism into a neural network, MP-PINN enables the mechanism to update in phases over time, reflecting the dynamics of the epidemics due to policy interventions. This hybrid approach outperforms pure data-driven or model-driven methods for both short-term and long-term forecasting, as demonstrated by experiments on COVID-19 waves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forecasting virus spreading in epidemics requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods use mechanistic models like the SIR family, which make strong assumptions about the underlying process. Data-driven methods can capture the generative process but fail in long-term forecasting due to data limitations. A new method called MP-PINN combines these approaches by updating the spreading mechanism over time. This hybrid approach works well for both short-term and long-term forecasting of COVID-19 waves. |
Keywords
» Artificial intelligence » Neural network » Time series
