دانلود کتاب COVID Transmission Modeling: An Insight into Infectious Diseases Mechanism

فهرست مطالب :

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Authors
1. Mathematical Modeling Approach to COVID-19: Vetted Real Data
1.1 Introduction
1.1.1 Main Objectives of This Book
1.2 Anatomical Structure of COVID-19 Virus
1.3 Virus Incubation Period
1.3.1 Disease Carriers
1.3.2 Case Fatality Rate (CFR, %)
1.3.3 COVID-19 Transmission Mechanisms
1.4 Epidemiological Aspects of nCov2019 (SARS-Cov-19)
1.5 Economic Impacts of Novel Coronavirus
1.6 Mathematical Model for the Prediction of Novel Coronavirus (nCov2019)
1.6.1 Variables Used for Model Building
1.6.2 Endemic and Epidemic Equilibria
1.7 Model Discussion
1.7.1 Model Conclusions
1.8 Epidemiological Model for the Estimation of Hazard Rate and Geometric Progression of nCov2019
1.8.1 Formulation of the Epidemiological Risk Assessment COVID Model
1.9 Epidemiological Model Approach of New Diseases
1.9.1 Model Formulation
1.9.1.1 Latent growth model of novel coronavirus
1.9.2 Gauss–Markov Theorem (GMT)
1.9.3 Maximum Likelihood Estimation (MLE) of Gauss–Markov Theorem (GMT)
1.9.4 Gauss–Markov Weighted Least Squares Analysis
1.10 Susceptible–Infective–Recovered (SIR) Epidemiological Model of COVID
1.10.1 Model Formulation
1.10.2 SIR Model Discussion
1.11 EP Model with Varying Population
1.11.1 Reproduction Number Approach to Binomial Distribution (R[sub(0)])
1.12 Machine Learning Model for SARS-Cov-19
1.12.1 Machine Learning Model
1.13 Models of Machine Learning
1.13.1 Measurement Error (u)
1.13.2 Stochastic Error (u)
1.13.3 Hidden Gauss–Markov Theorem (HGMT)
1.14 COVID-19 Mathematical Model Approach to Selective Sample
1.14.1 Model Formulation
1.15 Recommendations
1.16 Study Limitations
1.17 Conflict of Interest
References
2. Time Series Stochastic Projection Models for the Estimation of COVID Trend
2.1 Introduction
2.2 Time Series Stochastic Models
2.2.1 Exponential Smoothing by Bootstrap Techniques
2.2.2 Holt–Winters (HW) Triple Exponential Smoothing Method
2.3 ARIMA Forecasting Model Approach to COVID Progression Estimation
2.4 Model Discussion
2.5 Conclusions
2.6 Random Walk Markov Chain Stochastic Transient (RMCST) Model
2.7 Optimization of COVID Cases from Transition Matrix
2.8 Model Diagnostic Test
2.9 Discussion
2.10 Conclusions
References
3. Study of Anxiety and Fear of COVID-19
3.1 Introduction
3.2 Methods
3.3 Results
3.4 Discussion
3.5 Conclusions
References
4. COVID-19 Gene Sequencing Modeling
4.1 Introduction
4.2 Maxam and Gilbert Method
4.3 Sanger Sequencing
4.3.1 Chain Termination PCR
4.3.2 Separation Based on Size Using Gel Electrophoresis
4.3.3 Analysis of Gel and Identification of DNA Sequence
4.4 Long-Read Sequencing Methods
4.4.1 Single-Molecule Real-Time (SMRT) Sequencing
4.4.2 Nanopore DNA Sequencing
4.4.3 Merits of Nanopore DNA Sequencing
4.4.4 Massive Parallel Signature Sequencing (MPSS)
4.4.5 Statistical Methods for Testing MPSS Data
4.5 Cauchy Distribution
4.6 Exponential Distribution
4.7 Gamma Distribution
4.8 Log-Normal Distribution
4.9 Logistic Distribution
4.10 Poisson Distribution
4.11 Weibull Distribution Model
4.12 Next-Generation Sequencing
4.13 Illumina Solexa Sequencing
4.13.1 Procedure for Illumina Sequencing
4.14 Model Formulation
4.15 Pyrosequencing
4.16 Gene Sequencing Alignment
4.17 Alignment Parameters
4.18 COVID Sequencing
4.19 Gene Alignment and Its Applications
4.19.1 Global Alignment
4.19.2 Scoring Matrices
4.20 Whole Genomic Analysis (WGA)
4.21 Discussion
4.22 Conclusions
References
5. Real-Time PCR (RT-PCR) for COVID-19 Diagnosis and Changes in Threshold Cycle (C[sub(t)]) in Association with Different Parameters
5.1 Introduction
5.2 A High Threshold Value of C[sub(t)] in COVID-19
5.3 Model Formulation
5.4 Model Output
5.5 Discussion
5.6 Conclusions
References
6. COVID-19 Vaccination Modeling Approach to Public Health Policy
6.1 Introduction
6.2 What Else Does a Vaccine Contain?
6.3 Model Formulation—Need for Model Formulation
6.4 Methods
6.5 Determination of Vaccination Effects by Statistical Tools or Methods
6.6 Herd Immunity
6.7 COVID Vaccination Predictive Statistical Models
6.8 Model Construction
6.9 Projection Modeling of COVID Vaccination
6.10 Model Output
6.11 Real Probability of Bayes for Estimation of Vaccination Effect
6.12 Bayes Stochastic Vaccination Model
6.13 Discussion
6.14 Conclusions
6.15 Recommendations
References
7. Trend of COVID-19 Surge Projection
7.1 Introduction
7.2 Model Construction
7.3 Bayes Weighted Regression Model
7.4 Model Results
7.4.1 Projective Model of the Third Wave by Using I[sub(A)]Score
7.4.2 Calculation Summary
7.5 Discussion
7.6 Conclusions
References
8. Risk Analysis of COVID-19—Vetted by Real Data Sets
8.1 Introduction
8.2 Model Construction
8.3 Numerical Model Output
8.4 Discussion
8.5 Conclusions
References
9. Data-Driven Decision Support Model for COVID-19
9.1 Introduction
9.1.1 Communication-Driven Model
9.1.2 SARS-Cov-19 Data-Driven Model
9.1.3 Document-Driven Model
9.1.4 Knowledge-Driven Model
9.1.5 Model-Driven Decision Support System
9.2 Model Formulation
9.2.1 Rayleigh Distribution
9.2.2 Levy Distribution
9.2.3 Pareto Distribution
9.2.4 Laplace Distribution
9.2.5 Gamma Distribution
9.2.6 Logarithmic Distribution
9.2.7 Burnt Finger Poisson Distribution
9.2.8 Skellam (λ[sub(1)], λ[sub(2)]) Distribution
9.2.9 Reciprocal Distribution
9.2.10 Maxwell–Boltzmann (MB) Distribution
9.2.11 Inverse Gaussian Distribution
9.2.11.1 Model Results
9.3 Bayesian Decision Support Data-Driven Model (BDSDM)
9.4 Discussion
9.5 Conclusions
References
10. Age-Specific SARS-Cov-19 Incidence Modeling
10.1 Introduction
10.2 Model Construction
10.3 Model Output
10.4 Time Series Stochastic Model in the Prediction of the Second Wave of COVID in India
10.5 Model Formulation—Age-Specific Time Series
10.5.1 Model Output
10.6 Model 1
10.7 Discussion
10.8 Conclusions
References
11. National Health Policies and Their Perspectives on the Approach to Control COVID-19 and Other Infectious Diseases
11.1 Introduction
11.2 What Is Health Policy?
11.3 Methodological Considerations
11.3.1 Control of Infectious Disease COVID-19 Pandemic
11.3.2 Control of Tuberculosis by NHP
11.3.3 Control of HIV/AIDS
11.3.4 Leprosy Elimination
11.3.5 Vector-Borne Disease Control
11.4 Rationality of Health Policy During Emergency
11.5 Methods of Formulation of Policy Based on Different Social and Economic Attributes
11.6 Practical Approach to Implementation of Health Policy
11.6.1 A Collaborative Endeavour
11.6.2 A Broad Research Spectrum
11.7 Aligning Research with Need and Ensuring Quality
11.8 Government and Public Responsibilities to Safeguard Policy During an Emergency Situation
11.9 How to Formulate Health Policy Research
11.9.1 Objectives of the National Health Research Policy
11.10 Preparedness of Implementation Policy
11.10.1 What Is Health Policy Implementation?
11.10.2 What Is the Importance of Policy Implementation?
11.11 How to Implement Effective Policies and Procedures
11.11.1 Consultation
11.11.2 Tailor the Policy to Your Business
11.11.3 Define Obligations Clearly be Specific
11.11.4 Make the Policy Realistic
11.11.5 Publicize the Policies and Procedures
11.11.6 Train All Employees in Policies and Procedures
11.11.7 Be Consistent in Your Policy Implementation
11.11.8 Review All Policies and Procedures Regularly
11.11.9 Enforce the Workplace Policies and Procedures
11.12 The Challenges of Implementing Health Policy
11.12.1 What Happens During Policy Implementation?
11.12.2 Who is Involved in Policy Implementation?
11.13 Pilot Study for Implementation Policy
11.13.1 What Is a Pilot Implementation?
11.13.2 How Do You Implement a Pilot Program?
11.14 What Are Pilot Studies Used For?
11.14.1 What Are You Trying to Achieve?
11.15 Formulation of Uniform Civil Code to Frame Health Policy During the Pandemic Crisis
11.16 Propagation of Public Feedback for Policy Implementation
11.17 Barriers for Implementing Public Health and Social Measures to Prevent COVID-19
11.17.1 Lack of Safety Commitment from Public (Br-1)
11.17.2 Poor Safety Culture (Br-2)
11.17.3 Lack of Administrative Commitment and Support at Community Level (Br-3)
11.17.4 Lack of Strict Enforcement of WHO Regulations (Br-4)
11.17.5 Lack of Resources for Implementing Public Health and Social Measures (Br-5)
11.17.6 Lack of Medical Facilities at Community Level (Br-6)
11.17.7 Lack of Door-to-Door Services During Quarantine Period (Br-7)
11.17.8 Lack of Proper Communication between Health Advisors and the Public (Br-8)
11.17.9 Lack of Government Policies (Br-9)
11.17.10 Public Stigmatization (Br-10)
11.18 Responsibilities of Health Professionals, Bureaucrats, and Elected Members of Parliament to Control the Pandemic under Constitutional Framework
11.18.1 The Legal Framework for Combating a Pandemic in India
11.19 Indian Health Policy—Conclusions
11.20 International Health Policy
11.21 Redefining the “High-risk” Group
11.22 A Challenging Time to Deliver, but the Worst Moment to Stop
11.23 WHO Policy during the Pandemic Crisis
11.24 Take-Home Message
11.25 Conclusions
11.26 Recommendations
References
12. Statistical Ethics for Computational Biology and Medical Science
12.1 Introduction
12.1.1 Importance of Research Ethics
12.1.2 How to Recognize Predatory Journals
12.2 Statistical Ethics
12.2.1 Honesty
12.2.2 Objectivity
12.2.3 Integrity
12.2.4 Carefulness
12.2.5 Openness
12.3 Research Ethics in Human Interventions
12.4 Educate the Participants about Risks and Benefits
12.4.1 Confidentiality
12.4.2 Responsible Publication
12.4.3 Non-Discrimination
12.4.4 Human Subject Protection
12.4.5 Legality
12.5 Statistical Implications for Research Study
12.6 Selection of Appropriate Statistical Methods for Testing Research Hypotheses and Research Implications
12.7 Generalized Linear Model (GLM)
12.7.1 Model
12.7.2 Objective
12.7.3 Model Structure
12.7.4 Model Assumptions
12.7.5 Parameter Estimates and Interpretation
12.7.6 Model Fit
12.8 Model Selection
12.8.1 Random State Components
12.8.2 Systematic Components
12.8.3 Link Function ɳ or g(µ)
12.8.4 Assumptions of the Model
12.9 Model Diagnosis Test
12.10 Model Diagnosis by Information Criteria
12.11 Bayesian Analysis
12.11.1 Random Error
12.11.2 Systematic Error
12.11.3 Research Hypothesis Is Reliable, but Not Valid
12.11.4 New Research Findings Are Unrealistic Phenomena
12.12 Discussion
12.13 Conclusions
References
Index