Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics
- 1st Edition - February 1, 2026
- Editors: Sayooj Aby Jose, C. R. Jisha, Olfa Boubaker
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 2 3 4 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 2 3 5 - 7
Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the inters… Read more
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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.
- Presents advanced modeling techniques like fractional-order systems, stochastic analysis, and deep learning frameworks applied to real-world problems such as breast cancer, dengue, HBV, LSD, and COVID-19
- Provides practical solutions for disease control strategies, viscoelastic tissue modeling, and healthcare data security, fostering interdisciplinary applications of computational intelligence
- Offers a forward-looking perspective on the application of computational intelligence in healthcare, emphasizing sustainable monitoring and mitigation strategies
Researchers and academics in biomedical engineering, computational biology, and data science
Part I: Classical and Fractional Approaches to Infectious Disease Modeling
1. Mathematical and AI-Based Approaches in Epidemiology: Foundations and Frontiers
2. Comparative Numerical Methods for Infectious Disease Dynamics: Application to SEIR-type Models
3. Fractional Order Modeling and Stability Analysis of Vector-Borne Diseases: Application to Japanese Encephalitis Transmission
4. Optimal Control of Infectious Diseases Using Fractional Calculus: Application to Dengue Control via Atangana–Baleanu Model
Part II: Artificial Intelligence and Advanced Modeling in Epidemiology
5. Eco-Epidemiological Modeling with Memory Effects: Application to Fear, Quarantine, and Prey–Predator Interactions via Mittag-Leffler Kernel
6. Stochastic Analysis of Epidemic Models Under Random Perturbations: Application to SIR and SIRS Dual Epidemics
7. Deep Learning-Based Optimal Control Frameworks in Epidemiology: Application to Dengue Transmission Prediction and Control
8. AI-Driven Fractional Order Models for Emerging Viral Epidemics: Application to Oropouche Virus Outbreak Forecasting
Part III: Mathematical, Statistical, and AI-Based Models in Biomedicine and Healthcare
9. Explainable AI and Computational Intelligence in Healthcare: Application to Clinical Decision Support and Personalized Medicine
10. Soft Computing Models of Biological Tissue Dynamics: Application to Viscoelastic Behavior of Biological Tissues
11. Mathematical Modeling of Cancer Progression: Application to Ductal Carcinoma of the Breast
12. Modeling Immune Response and Antiviral Therapy Dynamics: Application to HBV Infection in Hepatic and Extrahepatic Sites
13. Statistical Modeling and Evaluation of Polyherbal Formulations: Application to Management of Diabetic Foot Ulcers
14. Conclusion | Prospects in computational epidemiology: challenges and emerging directions
1. Mathematical and AI-Based Approaches in Epidemiology: Foundations and Frontiers
2. Comparative Numerical Methods for Infectious Disease Dynamics: Application to SEIR-type Models
3. Fractional Order Modeling and Stability Analysis of Vector-Borne Diseases: Application to Japanese Encephalitis Transmission
4. Optimal Control of Infectious Diseases Using Fractional Calculus: Application to Dengue Control via Atangana–Baleanu Model
Part II: Artificial Intelligence and Advanced Modeling in Epidemiology
5. Eco-Epidemiological Modeling with Memory Effects: Application to Fear, Quarantine, and Prey–Predator Interactions via Mittag-Leffler Kernel
6. Stochastic Analysis of Epidemic Models Under Random Perturbations: Application to SIR and SIRS Dual Epidemics
7. Deep Learning-Based Optimal Control Frameworks in Epidemiology: Application to Dengue Transmission Prediction and Control
8. AI-Driven Fractional Order Models for Emerging Viral Epidemics: Application to Oropouche Virus Outbreak Forecasting
Part III: Mathematical, Statistical, and AI-Based Models in Biomedicine and Healthcare
9. Explainable AI and Computational Intelligence in Healthcare: Application to Clinical Decision Support and Personalized Medicine
10. Soft Computing Models of Biological Tissue Dynamics: Application to Viscoelastic Behavior of Biological Tissues
11. Mathematical Modeling of Cancer Progression: Application to Ductal Carcinoma of the Breast
12. Modeling Immune Response and Antiviral Therapy Dynamics: Application to HBV Infection in Hepatic and Extrahepatic Sites
13. Statistical Modeling and Evaluation of Polyherbal Formulations: Application to Management of Diabetic Foot Ulcers
14. Conclusion | Prospects in computational epidemiology: challenges and emerging directions
- Edition: 1
- Published: February 1, 2026
- Language: English
SJ
Sayooj Aby Jose
Dr. Sayooj Aby Jose is currently working as an Assistant Research Professor at the Institute for Pandemic Sciences AI.celerator (IPSAI), Artificial Intelligence Institute, Seoul National University, South Korea. Previously, he worked as a Postdoctoral Researcher in the Department of Statistics at Seoul National University. He earned his Ph.D. in Mathematics from Alagappa University, India, specializing in mathematical epidemiology, biostatistics, and artificial intelligence. He is actively involved in various international professional roles and projects and serves as an editor for seven reputed journals. Since 2023, he has been supervising and coordinating international student project programme (ISPP) and seminar series in collaboration with the Department of Mathematics, Faculty of Education, Phuket Rajabhat University, Thailand, with a specific focus on modeling in public health.
Affiliations and expertise
Assistant Research Professor at the Institute for Pandemic Sciences AI.celerator (IPSAI), Artificial Intelligence Institute, Seoul National University, South KoreaCJ
C. R. Jisha
Dr. Jisha C. R. is a Project Scientist II at the National Centre for Medium Range Weather Forecasting (NCMRWF), India, specializing in applied mathematics, particularly in partial differential equations, machine learning, and scientific computing. She earned her Ph.D. from SRM Institute of Science and Technology, focusing on transient PDEs, and holds a Bachelor's, Master's, and M.Phil. in Mathematics from Calicut University, Kerala. Dr. Jisha has postdoctoral experience at UNIST, South Korea, and has published her research in leading journals.
Affiliations and expertise
Project Scientist II, National Centre for Medium Range Weather Forecasting (NCMRWF), IndiaOB
Olfa Boubaker
Pr. Olfa Boubaker is a full professor at the National Institute of Applied Sciences and Technology (INSAT) at the University of Carthage, Tunisia, where she specializes in control theory, nonlinear systems, and robotics. She holds a Ph.D. in Electrical Engineering from the National Engineering School of Tunis and a Habilitation Universitaire in Control Engineering from the National Engineering School of Sfax. Prof. Boubaker is the series editor of the book series Medical Robots and Devices: New Developments and Advances."
Affiliations and expertise
Full Professor, National Institute of Applied Sciences and Technology (INSAT), University of Carthage, Tunisia