Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics

Part I: Classical and Fractional Approaches to Infectious Disease Modeling<br>1. Mathematical and AI-Based Approaches in Epidemiology: Foundations and Frontiers<br>2. Comparative Numerical Methods for Infectious Disease Dynamics: Application to SEIR-type Models<br>3. Fractional Order Modeling and Stability Analysis of Vector-Borne Diseases: Application to Japanese Encephalitis Transmission<br>4. Optimal Control of Infectious Diseases Using Fractional Calculus: Application to Dengue Control via Atangana–Baleanu Model<br><br>Part II: Artificial Intelligence and Advanced Modeling in Epidemiology<br>5. Eco-Epidemiological Modeling with Memory Effects: Application to Fear, Quarantine, and Prey–Predator Interactions via Mittag-Leffler Kernel<br>6. Stochastic Analysis of Epidemic Models Under Random Perturbations: Application to SIR and SIRS Dual Epidemics<br>7. Deep Learning-Based Optimal Control Frameworks in Epidemiology: Application to Dengue Transmission Prediction and Control<br>8. AI-Driven Fractional Order Models for Emerging Viral Epidemics: Application to Oropouche Virus Outbreak Forecasting<br><br>Part III: Mathematical, Statistical, and AI-Based Models in Biomedicine and Healthcare<br>9. Explainable AI and Computational Intelligence in Healthcare: Application to Clinical Decision Support and Personalized Medicine<br>10. Soft Computing Models of Biological Tissue Dynamics: Application to Viscoelastic Behavior of Biological Tissues<br>11. Mathematical Modeling of Cancer Progression: Application to Ductal Carcinoma of the Breast<br>12. Modeling Immune Response and Antiviral Therapy Dynamics: Application to HBV Infection in Hepatic and Extrahepatic Sites<br>13. Statistical Modeling and Evaluation of Polyherbal Formulations: Application to Management of Diabetic Foot Ulcers<br>14. Conclusion | Prospects in computational epidemiology: challenges and emerging directions