Emel Kurul , Huseyin Tunc , Murat Sari , Nuran Guzel
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引用次数: 0
Abstract
The study of disease spread often relies on compartmental models based on nonlinear differential equations, which typically require computationally intensive numerical algorithms, especially for parameter estimation. This paper introduces a deep neural network-based surrogate modeling (DNN-SM) approach, engineered to accurately replicate the behavior of epidemiological models while significantly reducing computational demands. This approach adeptly handles the complexities inherent in nonlinear models and optimizes parameter estimation efficiency. We demonstrate the efficacy of the DNN-SM through its application to various disease models, including the Susceptible–Infected–Recovered (SIR), Susceptible–Exposed–Infected–Recovered (SEIR), and the more complex Susceptible–Exposed–Presymptomatic–Asymptomatic–Symptomatic–Reported (SEPADR) models. The results reveal that our DNN-SM not only forecasts solution trajectories with high accuracy but also operates approximately ten times faster than traditional ODE solvers for forward problems. By comparing the parameter estimation results of the DNN-SM and ODE solvers, we show that the DNN-SM produces highly accurate results with much less computational costs. The DNN-SM has been validated using both short-term and long-term COVID-19 data from several European countries. The results demonstrate that the DNN-SM provides accurate trajectories with significantly lower computational cost compared to traditional numerical methods.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).