Mathematical Modeling and Soft Computing in Epidemiology

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Evolutionary Modeling of Dengue Fever with Incubation Period of Virus
	1.1 Introduction
	1.2 Basic Notions
		1.2.1 Padé Approximation
		1.2.2 Non-Stagnated Nelder–Mead Simplex Algorithm (NS-NMSA)
		1.2.3 Differential Evolution (DE)
	1.3 Model of Dengue Disease with Incubation Period of Virus
		1.3.1 Steady States of the Model
		1.3.2 Sensitivity of Basic Reproductive Number
	1.4 The Proposed DCMP Framework
	1.5 Results and Discussions
	1.6 Conclusion
	References
Chapter 2 Fuzzy-Genetic Approach to Epidemiology
	2.1 Introduction
	2.2 Genetic Epidemiology and Topology
	2.3 Unequal Crossover
	2.4 Mathematical Background
		2.4.1 Fuzzy Sets
		2.4.2 Fuzzy Pretopology
	2.5 Fuzzy Topological Properties of Recombination Space
		2.5.1 Mathematical Definition of Recombination Sets
		2.5.2 Unrestricted Unequal Crossover
		2.5.3 Fuzzy Pretopology in a Recombination Space
		2.5.4 Separation Properties
		2.5.5 Lindelofness and Compactness
		2.5.6 Connectedness
	2.6 Conclusion
	References
Chapter 3 Role of Mathematical Models in Physiology and Pathology
	3.1 Introduction
	3.2 Role of Mathematical Models in the Study of Brain Injury Problems
	3.3 Mathematical Modeling for Blood Flow in Human Artery/Vein
	3.4 Conclusion
	References
Chapter 4 Machine-Learned Regression Assessment of the HIV Epidemiological Development in Asian Region
	4.1 Introduction
	4.2 Mathematical Development of the HIV Epidemiology
		4.2.1 Techniques Implemented for Analysis
			4.2.1.1 Study of Phase Dynamics
			4.2.1.2 Distribution Fitting
			4.2.1.3 Goodness of Fit, Histogram, and Density Function
			4.2.1.4 Coefficient of Determination (R[sup(2)])
			4.2.1.5 Kolmogorov–Smirnov Test
		4.2.2 Analysis of Machine-Learned Regression Models
			4.2.2.1 L-1 Norm Regression-Learned Model
			4.2.2.2 Logistic Regression-Learned Model
			4.2.2.3 Poisson Regression-Learned Model
		4.2.3 Inferences of Multifaceted Epidemiological System
	4.3 Conclusion
	Acknowledgment
	References
Chapter 5 Mathematical Modeling to Find the Potential Number of Ways to Distribute Certain Things to Certain Places in Medical Field
	5.1 Introduction
	5.2 Real-Life Application of Mathematical Modeling of the Real-Life Situation Using Our Double Twin Domination Number of a Graph in Medical Field
	5.3 Double Twin Domination Number of Derived Graphs and Special Type of Graphs
	5.4 Conclusion
	References
Chapter 6 Fractional SIRI Model with Delay in Context of the Generalized Liouville–Caputo Fractional Derivative
	6.1 Introduction
	6.2 Fractional Calculus Tools and Stability Notions
	6.3 Presentation of SIRI Epidemic Model and Characteristics Numbers
	6.4 Existence and Uniqueness of the SIRI Model with Delay
	6.5 Stability of the SIRI Equation with Delay
	6.6 Conclusion
	References
Chapter 7 Optimal Control of a Nipah Virus Transmission Model
	7.1 Introduction
	7.2 Model Formulation
	7.3 Boundedness of Solutions
	7.4 Equilibrium Points and Basic Reproduction Number
	7.5 Stability of Equilibria
	7.6 Optimal Control of Nipah Virus Model
		7.6.1 Existence
		7.6.2 Construction of Optimal Control Problem
	7.7 Numerical Simulation
	7.8 Conclusion
	Appendix
	References
Chapter 8 Application of Eternal Domination in Epidemiology
	8.1 Introduction
	8.2 Epidemiology
	8.2.1 Real-Life Application in the Concept of Eternal Domination in Epidemiology
	8.3 Eternal Domination Number of Standard Graphs
	8.4 Eternal Domination Number of Some Product Related Graphs
	Acknowledgment
	References
Chapter 9 Numerical Analysis of Coupled Time-Fractional Differential Equations Arising in Epidemiological Models
	9.1 Introduction
	9.2 Preliminaries
	9.3 Basic Plan of HPTM for Coupled FDE in Epidemic Model
	9.3.1 Convergence Analysis
	9.3.2 Implementation of HPTM
	9.4 Numerical Results and Discussion
	9.5 Conclusion
	References
Chapter 10 Balancing of Nitrogen Mass Cycle for Healthy Living Using Mathematical Model
	10.1 Introduction
	10.2 Mathematical Model of Nitrogen Mass Cycle
	10.3 Mathematical Properties of the Deterministic Model
		10.3.1 Boundedness of the Nitrogen Mass Cycle
		10.3.2 Local Stability Analysis
		10.3.3 Global Stability Analysis
		10.3.4 Global Stability Analysis of Nitrogen Mass Cycle by Pseudo-Back-Propagation
	10.4 Nondeterministic Mathematical Model of Nitrogen Mass Cycle
		10.4.1 Description of the NonDeterministic Mathematical Model
		10.4.2 Nondeterministic Stability of the Positive Equilibrium
	10.5 Numerical Simulation
	10.6 Conclusion
	References
Chapter 11 Neutralizing of Nitrogen when the Changes of Nitrogen Content Is Rapid
	11.1 Introduction
	11.2 Description of the Mathematical Model
	11.3 Mathematical Properties of the Deterministic Model
		11.3.1 Boundedness of the Nitrogen Mass Cycle with Exponential Growth
		11.3.2 Local Stability Analysis
		11.3.3 Bifurcation
		11.3.4 Global Stability Analysis
		11.3.5 Global Stability Analysis of Nitrogen Mass Cycle with Exponential Growth by Pseudo-Back-Propagation
	11.4 Numerical Simulation
	11.5 Conclusion
	References
Chapter 12 Application of Blockchain Technology in Hospital Information System
	12.1 Introduction
	12.2 Hospital Information System
	12.3 Type of Hospital Information System
	12.4 Purpose of Hospital Information System
	12.5 Advantages of Hospital Information System
	12.6 Disadvantages of Hospital Information System
	12.7 Fragmented Health Data (Aggregation)
	12.8 Insufficient Financial Sources
	12.9 Maintenance by Different Departments
	12.10 Confidentiality Issues
	12.11 Acceptance Level Is Low
	12.12 Technical and Infrastructure Issues
	12.13 System Breakdown
	12.14 History of Blockchain Technology
	12.15 Fundamental Properties of Blockchain Technology
	12.16 Types of Blockchain Technology
	12.17 The Need of Blockchain Technology and its Advantages in Healthcare Sector
		12.17.1 Patient Data Management
	12.18 Payments and Reimbursement
	12.19 Drug and Medical Device Traceability
	12.20 Medical Research
	12.21 Regulatory Procedure
	12.22 Clinical Trials
	12.23 Disadvantages of Blockchain Technology in Healthcare Sector
	12.24 Conclusion
	References
Chapter 13 Complexity Analysis of Pathogenesis of Coronavirus Epidemiological Spread in the China Region
	13.1 Introduction
	13.2 Case Study: Pathogenesis of Coronavirus Epidemiological Spread in the China Region
	13.3 Phase, Time Progression, and Lyapunov Characteristics Exponent Analysis for the Prediction of its Spread
		13.3.1 Phase Analysis
		13.3.2 Lyapunov Characteristic Exponent (LCE)
		13.3.3 Algorithm for the Computation of LCE
		13.3.4 Attractors
		13.3.5 Nonlinear Regression
		13.3.6 Box–Cox Time Transformation Measure
		13.3.7 Autocorrelation and Partial Correlation Function (ACF & PACF)
		13.3.8 Augmented Dickey–Fuller Stationarity Test (ADF)
	13.4 Results and Discussion
	13.5 Conclusion
	Acknowledgments
	References
Chapter 14 A Mathematical Fractional Model to Study the Hepatitis B Virus Infection
	14.1 Introduction
	14.2 The Fractional Model
	14.3 Solution of the HBV Infection Model
	14.4 Convergence Analysis
	14.5 Result
	14.6 Conclusion
	References
Chapter 15 Nonlinear Dynamics of SARS-CoV2 Virus: India and Its Government Policy
	15.1 Introduction
	15.2 Brief Review
	15.3 SEIR Model
	15.4 Modification of SEIR Model: SEIRD Model
	15.5 Analytical Study
	15.6 How Can We Make the Model Better?
	15.7 Conclusion
	Acknowledgment
	References
Chapter 16 Ethical and Professional Issues in Epidemiology
	16.1 Introduction
	16.2 Immunization
	16.3 Epidemiological Surveillance
	16.4 Importance of Epidemiological Surveillance
	16.5 Ethical Issues in Epidemiology
		16.5.1 Mandates and Objections
		16.5.2 Vaccine Research and Testing
		16.5.3 Informed Consent
		16.5.4 Learning Issues
		16.5.5 Conflicts of Interest
		16.5.6 Scientific Malpractice
		16.5.7 Professional Issues in Epidemiology
		16.5.8 Epidemiological Ethics Roots
	References
Chapter 17 Cloud Virtual Image Security for Medical Data Processing
	17.1 Introduction
	17.2 Virtualization in Cloud Computing
		17.2.1 Importance of Virtualization
		17.2.2 Kerberos
			17.2.2.1 How Does Kerberos Authentication Works
			17.2.2.2 Challenges in Kerberos
	17.3 Data Center Technology
		17.3.1 Virtualization Technology
			17.3.1.1 Hardware Independence
			17.3.1.2 Server Consolidation
			17.3.1.3 Resource Replication
			17.3.1.4 Operating System-Based Virtualization
			17.3.1.5 Hardware-Based Virtualization
			17.3.1.6 Virtualization Management
	17.4 Virtual Machine Images
		17.4.1 System Virtual Machine
		17.4.2 Process Virtual Machine (Language Virtual Machine)
	17.5 Virtual Machine Switch and Session Management
		17.5.1 Virtual Machine Switch
		17.5.2 Session Management
	17.6 Medical Data in Cloud Computing
		17.6.1 e-Health Cloud Benefits
		17.6.2 e-Health Cloud Limitations
		17.6.3 Ownership and Privacy of Healthcare Information
		17.6.4 Authenticity
		17.6.5 Non-Repudiation
		17.6.6 Audit
		17.6.7 Access Control
		17.6.8 Cloud-Specific Security Aspects for e-Health Systems: The Case of VM Image Management
			17.6.8.1 VM Images as an Attack Vector
			17.6.8.2 The Way Forward
			17.6.8.3 Management of Virtual Image
			17.6.8.4 Image Archival and Destruction
	17.7 Literature Review
	17.8 Objective
		17.8.1 Implementation Model
		17.8.2 Terminologies Used in Implementation Model
			17.8.2.1 Access Server
			17.8.2.2 Authentication Server
			17.8.2.3 Storage Server
			17.8.2.4 Kerberos Security Mechanism
			17.8.2.5 Hardware Security Module (HSM)
			17.8.2.6 OpenStack
			17.8.2.7 Virtual Image Management
	17.9 Conclusion
	References
Chapter 18 Medical Data Security Using Blockchain and Machine Learning in Cloud Computing
	18.1 Introduction
		18.1.1 Cloud Computing
			18.1.1.1 Service Models
			18.1.1.2 Deployment Models
			18.1.1.3 Cloud Computing Security Challenges
		18.1.2 Cloud Computing Data Security and Threats Risks
			18.1.2.1 Security Risks
			18.1.2.2 Cloud Threats
			18.1.2.3 Privacy and Security
			18.1.2.4 Advantages of Cloud Computing Security
	18.2 Electronic Health Records in Cloud Computing
		18.2.1 Benefits and Risks of Cloud Computing in Healthcare
			18.2.1.1 Benefits
			18.2.1.2 Risk
		18.2.2 Challenges Faced by Blockchain Technology
			18.2.2.1 Security and Privacy Requirements of E-Health Data in Cloud
	18.3 Electronic Health Record Security Using Blockchain
		18.3.1 Medicalchain
			18.3.1.1 Medicalchain Features
			18.3.1.2 Process Flow
	18.4 Existing Healthcare Data Predictive Analytics Using Machine Learning Techniques
		18.4.1 Analytical Relation among EHR, AI, ML, and NLP
		18.4.2 Some Common Machine Learning EHR Algorithms
		18.4.3 Challenges for Machine Learning Approaches in EHR
		18.4.4 Techniques for EHR Tasks
	18.5 Literature Review
	18.6 Objectives of the Study
	18.7 Implementation Model
		18.7.1 Proposed Working Stages of Proposed Methodology
	18.8 Conclusion
	References
Chapter 19 Mathematical Model to Avoid Delay Wound Healing by Infinite Element Method
	19.1 Mathematical Modeling to Study Issue Temperature Deviation During Wound Healing
		19.1.1 Statement of the Problem
	19.2 Boundary Conditions at the Outer Surface and Inner Core
		19.2.1 Initial Condition
	19.3 Use of the Finite Element Method and the Infinite Element Method
	19.4 Shape Functions
	19.5 Matrix Formation
	19.6 Assembly of Elements
	19.7 Formation of Simultaneous Differential Equations in Time
	19.8 Numerical Results and Discussions
	References
Chapter 20 Data Classicafitiion Framework for Medical Data through Machine Learning Techniques in Cloud Computing
	20.1 Introduction
		20.1.1 Features of Cloud Computing
		20.1.2 Advantages of Cloud Computing
		20.1.3 Categories of Service Model
		20.1.4 Types of Cloud
	20.2 Data Storage in Cloud Computing
		20.2.1 Storage Devices
		20.2.2 Storage Classes of Cloud
		20.2.3 Creating Cloud Storage System
		20.2.4 Virtual Storage Containers
	20.3 Virtualization in Cloud Computing
	20.4 Security Issue in Cloud Computing
		20.4.1 Common Security Requirement
	20.5 Data Classification
		20.5.1 Classification Applied to Information Types
		20.5.2 Medical Data set Classification
		20.5.3 Challenges of Medical Data Classification
		20.5.4 EHR Information Extraction through Machine Learning Approaches
		20.5.5 Security and Privacy of Classified Data
	20.6 Literature Review
	20.7 Objectives of the Study
	20.8 Implementation Model
		20.8.1 Proposed Model
	20.9 Conclusion
	References
Index