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Forecasting Disease Spread Using the Stochastic Model and Kalman Filter | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 31 فروردین 1405 اصل مقاله (1.36 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2025.67639.3231 | ||
| نویسندگان | ||
| Javad Hajizadeh؛ Parisa Nabati* | ||
| Faculty of Science, Urmia University of Technology, Urmia, Iran. | ||
| چکیده | ||
| This manuscript investigates the use of stochastic differential equations alongside Kalman filtering methodologies within the SIRD framework to enhance the predictive accuracy of infectious disease epidemic modeling. Leveraging empirical data from the COVID-19 pandemic, the study incorporates stochasticity into transmission dynamics by introducing perturbations in the infection rate. A comparative analysis of both deterministic and stochastic versions of the model is presented. The extended Kalman filter is employed to infer system states from noisy observations, enabling real-time epidemic monitoring. Simulation results show that applying the Kalman filter significantly improves predictive accuracy and model fidelity, closely aligning with the simulated data. This integrated modeling approach offers a robust framework for public health planning, providing more reliable forecasts and supporting the timely implementation of intervention measures. | ||
| کلیدواژهها | ||
| Stochastic Differential Equations؛ Kalman Filter؛ Epidemic Modeling؛ Prediction؛ Infectious Diseases | ||
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آمار تعداد مشاهده مقاله: 21 تعداد دریافت فایل اصل مقاله: 27 |
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