Jul 2026· Computer Methods and Programs in Biomedicine· Vol 285, pp.
109535
· 0 citations· 44 references
Medicine
TL;DR
The results show that M-SEINN outperformed the Euler SEINN and EINN and integrates stochastic differential equations, emphasizing the necessity of M-SEINN adoption for parameter estimation and public health decisions for epidemic control.
Abstract
Background
AND
Objective
Epidemiological dynamics require precise mathematical modeling to guide public health actions, especially for viral diseases such as monkeypox, where data uncertainty and nonlinear transmission patterns present significant challenges. In this context, we suggest a novel approach using stochastic epidemic models and deep neural networks.
Methods
In fact, we introduce the Epidemiologically Informed Neural Network (EINN), which uses the classical SIRD model to capture the dynamics of human-to-human transmission of Mpox. Then, we extend to a novel Milstein stochastic epidemiologically informed neural network (M-SEINN), which integrates stochastic differential equations.
Results
Our results show that M-SEINN outperformed the Euler SEINN and EINN. At 5% noise in the out-of-sample evaluation, it achieves the lowest RMSE of 3.9569 and the best MAPE of 14.92% for cumulative cases, while at 10% noise in the sample evaluation, the daily case RMSE is 11.29, compared to 12.98 and 14.25, respectively. Statistical analysis demonstrated narrow Bootstrap CIs and a medium-large Cohen's d (0.65-1.02).
Conclusion
These findings emphasize the necessity of M-SEINN adoption for parameter estimation and public health decisions for epidemic control.
This work introduces Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens and demonstrates superior performance across a range of measures.
Laura M Guzman-Rincon, George R.E. Bradley, Joel Kandiah et al.· 0 citations
Infectious diseases exhibit complex and rapidly evolving transmission dynamics, requiring modeling approaches that can accurately capture these mechanisms. The SIRS-D compartmental model provides a suitable framework, as it incorporates temporary immunity and disease-induced mortality within the epidemic process. Accurate parameter estimation is essential for quantifying the transmission rate, recovery rate, waning immunity rate, and mortality rate, which collectively govern the system behavior. Among existing estimation methods, Physics-Informed Neural Networks (PINNs) offer significant advantages by integrating observational data with the underlying structure of differential equations, thereby preserving physical consistency while maintaining robustness under imperfect data conditions. In this study, PINNs are employed to estimate the parameters of the SIRS-D model using synthetic data generated through the fourth-order Runge–Kutta (RK4) method to ensure stable and consistent numerical solutions. To better represent real-world measurement conditions, 5% noise is added to the synthetic data, introducing realistic variability into the training process. The results demonstrate that PINNs successfully reconstruct the trajectories of S(t), I(t), R(t), and D(t) with low prediction errors. The model achieves MAE values of 0.0065 (S), 0.0067 (I), 0.0208 (R), and 0.0043 (D), with corresponding RMSE values of 0.0090, 0.0074, 0.0253, and 0.0058. Moreover, the estimated parameters closely match the true values, yielding ????????=0.5031, ????=0.0996, ????=0.0095, and ????=0.0149, demonstrating strong parameter identification capability. These findings confirm that PINNs constitute a reliable and accurate framework for analyzing infectious disease dynamics and offer promising potential for extension to more complex epidemiological models and real-world datasets.
Fitri Cahyani, Abdurakhman Abdurakhman, Chyntia Meininda Anjanni· The eurasia proceedings of s...· 0 citations
This study proposes a Fractional-Order Physics-Informed Neural Network (FPINN) framework for solving inverse parameter estimation problems in both fractional SIR and augmented SEIR epidemiological models and demonstrates that the proposed method accurately reconstructs epidemic trajectories and captures the influence of memory effects on disease evolution.
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A Physics-Informed Neural Network based framework for an e-epidemic SI1I2R model of computer-virus spread that incorporates a possibly transmissible class, an amply transmissible class, and direct transmission, allowing nodes to be initially compromised without contact is developed.
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The findings suggest that the combination of fractional-order memory with physics-informed learning constitutes an effective and interpretable model for dengue forecasting, providing a sound model for epidemic forecasting.
Harshit, Priyanka Harjule· International Conference on...· 0 citations
Experiments with synthetic data and COVID-19 surveillance data show that SUC–PINN recovers plausible hidden infection trajectories, yields stable parameter estimates, and provides accurate short-term forecasts, support SUC–PINN as a practical computational approach for inverse modeling and prediction in partially observed epidemic dynamics.
U. M. Rifanti, N. Susyanto, Ratinan Boonklurb· Advances in Complex Systems· 0 citations