Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the intersection of mathematics, biology, and medicine. This book delves into advanced mathematical modeling, artificial intelligence, and computational intelligence, providing essential tools for understanding and managing complex disease dynamics. Covering a wide range of topics, including fractional-order modeling, optimal control strategies, and privacy-preserving technologies, it addresses critical challenges in public health and healthcare systems. With contributions from leading experts, this volume bridges theoretical advancements and practical applications, making it an invaluable resource for researchers, healthcare professionals, and academics seeking interdisciplinary solutions to global health issues.
1. Introduction - p. xix 2. Part 1: Classical and fractional approaches to infectious disease modeling - p. P1 3. Chapter 1: Mathematical and AI-based approaches in epidemiology: Foundations and frontiers - p. 3 4. Chapter 2: Comparative numerical methods for infectious disease dynamics: Application to SEIR-type models - p. 29 5. Chapter 3: Fractional-order modeling and stability analysis of vector-borne diseases: Application to Japanese encephalitis transmission - p. 57 6. Chapter 4: Optimal control of infectious diseases using fractional calculus: Application to dengue control via Atangana–Baleanu model - p. 85 7. Part 2: Artificial intelligence and advanced modeling in epidemiology - p. P2 8. Chapter 5: Eco-epidemiological modeling with memory effects: Application to fear, quarantine, and prey–predator interactions via Mittag-Leffler kernel - p. 113 9. Chapter 6: Stochastic analysis of epidemic models under random perturbations: Application to SIR and SIRS dual epidemics - p. 143 10. Chapter 7: Deep learning-based optimal control frameworks in epidemiology: Application to dengue transmission prediction and control - p. 181 11. Chapter 8: AI-driven fractional order models for emerging viral epidemics: Application to Oropouche virus outbreak forecasting - p. 211 12. Part 3: Mathematical, statistical, and AI-based models in biomedicine and healthcare - p. P3 13. Chapter 9: Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine - p. 245 14. Chapter 10: Soft computing models of biological tissue dynamics: Application to viscoelastic behavior of biological tissues - p. 273 15. Chapter 11: Mathematical modeling of cancer progression: Application to ductal carcinoma of the breast - p. 295 16. Chapter 12: Modeling immune response and antiviral therapy dynamics: Application to HBV infection in hepatic and extrahepatic sites - p. 325 17. Chapter 13: Statistical modeling and evaluation of polyherbal formulations: Application to management of diabetic foot ulcers - p. 351 18. Chapter 14: Conclusion | Prospects in computational epidemiology: Challenges and emerging directions - p. 375 19. Index - p. 377
Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the intersection of mathematics, biology, and medicine. This book delves into advanced mathematical modeling, artificial intelligence, and computational intelligence, providing essential tools for understanding and managing complex disease dynamics. Covering a wide range of topics, including fractional-order modeling, optimal control strategies, and privacy-preserving technologies, it addresses critical challenges in public health and healthcare systems. With contributions from leading experts, this volume bridges theoretical advancements and practical applications, making it an invaluable resource for researchers, healthcare professionals, and academics seeking interdisciplinary solutions to global health issues.
1. Introduction - p. xix 2. Part 1: Classical and fractional approaches to infectious disease modeling - p. P1 3. Chapter 1: Mathematical and AI-based approaches in epidemiology: Foundations and frontiers - p. 3 4. Chapter 2: Comparative numerical methods for infectious disease dynamics: Application to SEIR-type models - p. 29 5. Chapter 3: Fractional-order modeling and stability analysis of vector-borne diseases: Application to Japanese encephalitis transmission - p. 57 6. Chapter 4: Optimal control of infectious diseases using fractional calculus: Application to dengue control via Atangana–Baleanu model - p. 85 7. Part 2: Artificial intelligence and advanced modeling in epidemiology - p. P2 8. Chapter 5: Eco-epidemiological modeling with memory effects: Application to fear, quarantine, and prey–predator interactions via Mittag-Leffler kernel - p. 113 9. Chapter 6: Stochastic analysis of epidemic models under random perturbations: Application to SIR and SIRS dual epidemics - p. 143 10. Chapter 7: Deep learning-based optimal control frameworks in epidemiology: Application to dengue transmission prediction and control - p. 181 11. Chapter 8: AI-driven fractional order models for emerging viral epidemics: Application to Oropouche virus outbreak forecasting - p. 211 12. Part 3: Mathematical, statistical, and AI-based models in biomedicine and healthcare - p. P3 13. Chapter 9: Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine - p. 245 14. Chapter 10: Soft computing models of biological tissue dynamics: Application to viscoelastic behavior of biological tissues - p. 273 15. Chapter 11: Mathematical modeling of cancer progression: Application to ductal carcinoma of the breast - p. 295 16. Chapter 12: Modeling immune response and antiviral therapy dynamics: Application to HBV infection in hepatic and extrahepatic sites - p. 325 17. Chapter 13: Statistical modeling and evaluation of polyherbal formulations: Application to management of diabetic foot ulcers - p. 351 18. Chapter 14: Conclusion | Prospects in computational epidemiology: Challenges and emerging directions - p. 375 19. Index - p. 377
