دانلود کتاب Modeling and Control of Infectious Diseases in the Host: With MATLAB and R

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Cover Modeling and Control of Infectious Diseases in the Host: With Matlab and R Copyright Dedication About the Author Preface Acknowledgments Part 1: Theoretical Biology Principles 1 Introduction 1.1 Modeling and Control of Infectious Diseases 1.2 Basics of Immunology 1.3 Basics of Virology 1.4 Viral Mutation and Drug Resistance 2 Mathematical Modeling Principles 2.1 Mathematical Modeling 2.2 Mathematical Preliminaries 2.3 Dynamical Systems 2.4 Population Modeling 3 Model Parameter Estimation 3.1 Parameter Fitting 3.2 Experimental Data 3.3 Cost Function 3.4 Optimization Problem Running the Optimizing Toolbox 3.5 Identifiability 3.6 Bootstrapping Parameters 3.7 Sources of Errors and Limitations Choices of Initial Conditions in Skew Parameter Estimates Measurement Error Impairs the Parameter Estimates Data Asynchrony Generates Overlooked Measurement Error Sparse and Scarce Data Aggravate the Parameter Estimates Performance by Different Parameter Estimation Algorithms 3.8 Concluding Remarks Part 2: Modeling Host Infectious Diseases 4 Modeling Influenza Virus Infection 4.1 Influenza Infection 4.2 Modeling Influenza Infection 4.3 Influenza Infection Model With Immune Response 4.4 Postinfluenza Susceptibility to Pneumococcal Coinfection 4.5 Concluding Remarks 5 Modeling Ebola Virus Infection 5.1 Ebola Infection 5.2 In Vitro Ebola Virus Infection Model 5.3 In Vivo Ebola Virus Infection and Vaccination 5.4 Concluding Remarks 6 Modeling HIV Infection 6.1 HIV Infection 6.2 HIV Disease Progression 6.3 How Does HIV Cause AIDS? 6.4 Mathematical Modeling of HIV Infection 6.5 The Three Stages in HIV Infection 6.6 Concluding Remarks 7 HIV Evolution During Treatment 7.1 Antiretroviral Drugs for HIV Infection 7.2 Guidelines for HAART Treatment 7.3 Including HAART in Mathematical Models 7.4 Basic Viral Mutation Treatment Models 7.5 Model With Reservoirs That Replicate Virus Frequently 7.6 Mutation Model With Latent CD4+ T Reservoirs 7.7 Concluding Remarks Part 3: Advanced Topics in Control Theory 8 Optimal Therapy Scheduling 8.1 Optimal Control Background Hamilton-Jacobi Theory Maximum Principle 8.2 Positive Switched Linear Systems 8.3 Optimal Control for Positive Switched Systems 8.4 Optimal Control to Mitigate HIV Escape Invariant Subspaces Generalized Symmetry Case for Viral Mutation General Solution for a 4-Variant Model 8.5 Restatement as an Optimization Problem 8.6 Dynamic Programming for Positive Switched Systems Algorithm 1: Reverse Time Solution Algorithm 2: Box Constraint Algorithm Algorithm 3: Joint Forward/Backward Box Constraint Algorithm Numerical Results for Discrete-Time Optimal Control Computational Resources 8.7 Concluding Remarks 9 Suboptimal Therapy Scheduling 9.1 Control of Switched Systems 9.2 Continuous-Time Guaranteed Cost Control 9.3 Discrete-Time Guaranteed Cost Control 9.4 Model Predictive Control 9.5 Mitigating HIV Escape Simulations 9.6 Concluding Remarks 10 PK/PD-based Impulsive Control 10.1 Introduction 10.2 Inverse Optimal Impulsive Control 10.3 Tailoring Influenza Treatment 10.4 Concluding Remarks Bibliography Index Back Cover