Epidemic dynamics prediction using fractional SIRD and deep learning
Ramsha Shafqat, Kinda Abuasbeh, Salma Trabelsi, Marwa Balti · 2025
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Description
Ramsha Shafqat, Kinda Abuasbeh, Salma Trabelsi, and Marwa Balti build an epidemic forecasting model with memory. Their setup gives the classic SIRD equations a neural network partner, in a two thousand twenty-five math modeling paper. Standard models forget the past; real epidemics do not.
The fractional-order model uses a normalized Caputo-Fabrizio derivative and tracks deaths in their own compartment. The authors prove existence, uniqueness, and positivity of solutions and supply a numerical scheme. A deep neural network then learns the fractional dynamics, reaching very low prediction errors across compartments. Model runs show how the memory parameter shapes the curves. The setup targets more reliable forecasts for public health choices.
Catalog record
| CREATOR | Ramsha Shafqat, Kinda Abuasbeh, Salma Trabelsi, Marwa Balti |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 155 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |