Mathematical Modeling and Soft Computing in Epidemiology [1 ed.] 0367903059, 9780367903053
This book describes the uses of different mathematical modeling and soft computing techniques used in epidemiology for e
308 38 13MB
English Pages 440 Year 2020
![Mathematical Modeling and Soft Computing in Epidemiology [1 ed.]
0367903059, 9780367903053](https://dokumen.pub/img/200x200/mathematical-modeling-and-soft-computing-in-epidemiology-1nbsped-0367903059-9780367903053.jpg)
Table of contents :
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
