Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics
- 1st Edition - February 1, 2026
- Imprint: Academic Press
- 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
Preface Introduction | Mathematical and AI-based approaches in epidemiology: a new era of disease modelling
Part I: Mathematical and Computational Models for Infectious Disease Dynamics
1. Sensitivity Analysis and Parameter Estimation in a SIR Model of HBV
2. Stability and Control of a Nonlinear Fractional Model for Japanese Encephalitis Transmission
3. Optimization of Dengue Control Strategies Using the Atangana-Baleanu Fractional Order Model
4. Eco-epidemiological modeling: the role of fear effect and quarantine in prey-predator dynamics using the Mittag-Leffler kernel
5. Stochastic Analysis of Dual Epidemics: SIR and SIRS Models under Random Perturbations
6. A Deep Learning-Based Optimal Control Framework for Dengue Transmission: Analytical and Numerical Overviews
7. AI-Driven Fractional Order Models for Predicting and Controlling Oropouche Virus Epidemics
Part II: Computational Intelligence and Mathematical Models for Disease Mechanisms
8. Revolutionizing Healthcare: Advanced Computational Intelligence and Explainable AI (XAI) Systems
9. Soft Computing Approaches for Modeling the Viscoelastic Nature of Biological Tissues
10. Mathematical Modeling of Breast Cancer: Insights into Ductal Carcinoma Progression and Treatment Strategies
11. Modeling HBV Therapy and Adaptive Immune Response in Hepatic and Extrahepatic Tissues
12. Statistical Analysis of Polyherbal Formulations in the Management of Diabetic Foot Ulcers
13: Conclusion | Prospects in computational epidemiology: challenges and emerging directions
Part I: Mathematical and Computational Models for Infectious Disease Dynamics
1. Sensitivity Analysis and Parameter Estimation in a SIR Model of HBV
2. Stability and Control of a Nonlinear Fractional Model for Japanese Encephalitis Transmission
3. Optimization of Dengue Control Strategies Using the Atangana-Baleanu Fractional Order Model
4. Eco-epidemiological modeling: the role of fear effect and quarantine in prey-predator dynamics using the Mittag-Leffler kernel
5. Stochastic Analysis of Dual Epidemics: SIR and SIRS Models under Random Perturbations
6. A Deep Learning-Based Optimal Control Framework for Dengue Transmission: Analytical and Numerical Overviews
7. AI-Driven Fractional Order Models for Predicting and Controlling Oropouche Virus Epidemics
Part II: Computational Intelligence and Mathematical Models for Disease Mechanisms
8. Revolutionizing Healthcare: Advanced Computational Intelligence and Explainable AI (XAI) Systems
9. Soft Computing Approaches for Modeling the Viscoelastic Nature of Biological Tissues
10. Mathematical Modeling of Breast Cancer: Insights into Ductal Carcinoma Progression and Treatment Strategies
11. Modeling HBV Therapy and Adaptive Immune Response in Hepatic and Extrahepatic Tissues
12. Statistical Analysis of Polyherbal Formulations in the Management of Diabetic Foot Ulcers
13: Conclusion | Prospects in computational epidemiology: challenges and emerging directions
- Edition: 1
- Published: February 1, 2026
- Imprint: Academic Press
- Language: English
SJ
Sayooj Aby Jose
Dr. Sayooj Aby Jose is a postdoctoral researcher in the Department of Statistics at Seoul National University, South Korea,. He previously held a postdoctoral position in the Department of Mathematics at Phuket Rajabhat University, Thailand, where he also began a visiting faculty role in March 2024. Dr. Jose earned his Ph.D. in Mathematics from Alagappa University, India, specializing in epidemiology, stability analysis, biostatistics, and mathematical modeling. He has published over 35 research papers and received the IMU Breakout Graduate Fellowship in 2021. His academic contributions include serving on editorial boards for several journals and organizing numerous conferences. He will also serve as a Visiting Research Professor in Mathematical Epidemiology at the Public Health Innovations & Research Center in India starting October 2024. Dr. Jose is committed to academic collaboration and has participated in various national and international research projects.
Affiliations and expertise
Postdoctoral Researcher, Department of Statistics, Seoul National University, Republic of 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. She is also a mentor in the Association for Women in Mathematics and leads the research department at AMICAISC, focusing on AI-driven solutions. An active member of AMS and SIAM, she has organized international conferences and given invited talks, contributing significantly to the fields of wave-structure interactions and coastal infrastructure. 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. She is also a mentor in the Association for Women in Mathematics and leads the research department at AMICAISC, focusing on AI-driven solutions. An active member of AMS and SIAM, she has organized international conferences and given invited talks, contributing significantly to the fields of wave-structure interactions and coastal infrastructure.
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. Professor Boubaker has led numerous research projects in sustainable development, including medical robotics and green energy. She has authored over 150 peer-reviewed papers and several books, and she is the founder/editor of the book series Medical Robots and Devices. Additionally, she serves as an associate editor for the journal Robotica and the International Journal of Advanced Robotic Systems, contributing to various scientific journals and mentoring numerous engineering graduates.
Affiliations and expertise
Full Professor, National Institute of Applied Sciences and Technology (INSAT), University of Carthage, Tunisia