Artificial neural networks for stability analysis and simulation of delayed rabies spread models
Rabies remains a significant public health challenge, particularly in areas with substantial dog populations, necessitating a deeper understanding of its transmission dynamics for effective control strategies. This study addressed the complexity of rabies spread by integrating two critical delay eff...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-11-01
|
| Series: | AIMS Mathematics |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241599 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590770455969792 |
|---|---|
| author | Ramsha Shafqat Ateq Alsaadi |
| author_facet | Ramsha Shafqat Ateq Alsaadi |
| author_sort | Ramsha Shafqat |
| collection | DOAJ |
| description | Rabies remains a significant public health challenge, particularly in areas with substantial dog populations, necessitating a deeper understanding of its transmission dynamics for effective control strategies. This study addressed the complexity of rabies spread by integrating two critical delay effects—vaccination efficacy and incubation duration—into a delay differential equations model, capturing more realistic infection patterns between dogs and humans. To explore the multifaceted drivers of transmission, we applied a novel framework using piecewise derivatives that incorporated singular and non-singular kernels, allowing for nuanced insights into crossover dynamics. The existence and uniqueness of solutions was demonstrated using fixed-point theory within the context of piecewise derivatives and integrals. We employed a piecewise numerical scheme grounded in Newton interpolation polynomials to approximate solutions tailored to handle singular and non-singular kernels. Additionally, we leveraged artificial neural networks to split the dataset into training, testing, and validation sets, conducting an in-depth analysis across these subsets. This approach aimed to expand our understanding of rabies transmission, illustrating the potential of advanced mathematical tools and machine learning in epidemiological modeling. |
| format | Article |
| id | doaj-art-b6d8af2251db44ef816025f263eaf5d3 |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-b6d8af2251db44ef816025f263eaf5d32025-01-23T07:53:24ZengAIMS PressAIMS Mathematics2473-69882024-11-01912334953353110.3934/math.20241599Artificial neural networks for stability analysis and simulation of delayed rabies spread modelsRamsha Shafqat0Ateq Alsaadi1Department of Mathematics and Statistics, The University of Lahore, Sargodha 40100, PakistanDepartment of Mathematics and Statistics, College of Science, Taif University, P. O. Box 11099, Taif 21944, Saudi ArabiaRabies remains a significant public health challenge, particularly in areas with substantial dog populations, necessitating a deeper understanding of its transmission dynamics for effective control strategies. This study addressed the complexity of rabies spread by integrating two critical delay effects—vaccination efficacy and incubation duration—into a delay differential equations model, capturing more realistic infection patterns between dogs and humans. To explore the multifaceted drivers of transmission, we applied a novel framework using piecewise derivatives that incorporated singular and non-singular kernels, allowing for nuanced insights into crossover dynamics. The existence and uniqueness of solutions was demonstrated using fixed-point theory within the context of piecewise derivatives and integrals. We employed a piecewise numerical scheme grounded in Newton interpolation polynomials to approximate solutions tailored to handle singular and non-singular kernels. Additionally, we leveraged artificial neural networks to split the dataset into training, testing, and validation sets, conducting an in-depth analysis across these subsets. This approach aimed to expand our understanding of rabies transmission, illustrating the potential of advanced mathematical tools and machine learning in epidemiological modeling.https://www.aimspress.com/article/doi/10.3934/math.20241599rabies spread modelpiecewise derivativecaputo derivativeatangana-baleanu-caputo derivativenewton polynomials numerical methodartificial neural network |
| spellingShingle | Ramsha Shafqat Ateq Alsaadi Artificial neural networks for stability analysis and simulation of delayed rabies spread models AIMS Mathematics rabies spread model piecewise derivative caputo derivative atangana-baleanu-caputo derivative newton polynomials numerical method artificial neural network |
| title | Artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| title_full | Artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| title_fullStr | Artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| title_full_unstemmed | Artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| title_short | Artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| title_sort | artificial neural networks for stability analysis and simulation of delayed rabies spread models |
| topic | rabies spread model piecewise derivative caputo derivative atangana-baleanu-caputo derivative newton polynomials numerical method artificial neural network |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241599 |
| work_keys_str_mv | AT ramshashafqat artificialneuralnetworksforstabilityanalysisandsimulationofdelayedrabiesspreadmodels AT ateqalsaadi artificialneuralnetworksforstabilityanalysisandsimulationofdelayedrabiesspreadmodels |