Biomedical
Peer Reviewed
PINNs are neural networks that incorporate both data and the governing equations of a physical system. In epidemiological models, they are used to estimate time-varying parameters and state variables by minimizing a loss function based on both observed data and the residuals of the model equations.
PINNs can simultaneously estimate multiple time-dependent parameters, infer dynamics using different types of data jointly, provide future projections, and train even with gaps or uncertainties in data quality. They offer a deterministic approach that can be more efficient than traditional Bayesian methods.
The split approach trains the PINN in two steps: first on epidemiological data, then on model residuals. The joint approach minimizes a combined loss function on data and residuals simultaneously. The split approach often converges faster and provides more stable results.
PINNs can accurately estimate time-varying transmission rates, especially after initial periods of an outbreak. They perform well in both synthetic test cases and real-world scenarios, such as the Italian COVID-19 epidemic data, providing estimates comparable to established methods.
PINNs may struggle with estimating initial conditions and early outbreak dynamics. They also lack built-in uncertainty quantification, unlike Bayesian methods. Performance can degrade with limited or noisy data, and the approach may need adaptation for more complex compartmental models.
Show by month | Manuscript | Video Summary |
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2025 May | 2 | 2 |
2025 April | 92 | 92 |
2025 March | 81 | 81 |
2025 February | 67 | 67 |
2025 January | 69 | 69 |
2024 December | 24 | 24 |
Total | 335 | 335 |
Show by month | Manuscript | Video Summary |
---|---|---|
2025 May | 2 | 2 |
2025 April | 92 | 92 |
2025 March | 81 | 81 |
2025 February | 67 | 67 |
2025 January | 69 | 69 |
2024 December | 24 | 24 |
Total | 335 | 335 |