كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R
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  كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R

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 كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R  Empty
مُساهمةموضوع: كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R     كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R  Emptyالثلاثاء 06 أكتوبر 2020, 11:03 am

أخوانى فى الله
أحضرت لكم كتاب
Modeling and Control of Infectious Diseases in the Host With Matlab and R
Esteban A. Hernandez-Vargas
Frankfurt am Main, Germany
Series Editor
Edgar Sánchez  

 كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R  M_a_c_11
و المحتوى كما يلي :


Contents
About the Author . ix
Preface . xi
Acknowledgments . xiii
Part 1 Theoretical Biology Principles
Chapter 1 Introduction .3
1.1 Modeling and Control of Infectious Diseases 3
1.2 Basics of Immunology . 5
1.3 Basics of Virology . 13
1.4 Viral Mutation and Drug Resistance 15
CHAPTER 2 Mathematical Modeling Principles .19
2.1 Mathematical Modeling . 19
2.2 Mathematical Preliminaries . 21
2.3 Dynamical Systems . 27
2.4 Population Modeling . 29
CHAPTER 3 Model Parameter Estimation 35
3.1 Parameter Fitting . 35
3.2 Experimental Data . 38
3.3 Cost Function . 40
3.4 Optimization Problem 42
3.5 Identifiability . 46
3.6 Bootstrapping Parameters 50
3.7 Sources of Errors and Limitations . 51
3.8 Concluding Remarks 59
PART 2 MODELING HOST INFECTIOUS DISEASES
CHAPTER 4 Modeling Influenza Virus Infection .65
4.1 Influenza Infection . 65
4.2 Modeling Influenza Infection . 67
4.3 Influenza Infection Model With Immune Response . 73
4.4 Postinfluenza Susceptibility to Pneumococcal Coinfection . 76
4.5 Concluding Remarks 81
CHAPTER 5 Modeling Ebola Virus Infection .85
5.1 Ebola Infection . 85
5.2 In Vitro Ebola Virus Infection Model 87
5.3 In Vivo Ebola Virus Infection and Vaccination 94
5.4 Concluding Remarks 101
viiviii Contents
CHAPTER 6 Modeling HIV Infection 105
6.1 HIV Infection . 105
6.2 HIV Disease Progression . 107
6.3 How Does HIV Cause AIDS? 108
6.4 Mathematical Modeling of HIV Infection . 110
6.5 The Three Stages in HIV Infection 111
6.6 Concluding Remarks 125
CHAPTER 7 HIV Evolution During Treatment .129
7.1 Antiretroviral Drugs for HIV Infection 129
7.2 Guidelines for HAART Treatment . 131
7.3 Including HAART in Mathematical Models 134
7.4 Basic Viral Mutation Treatment Models 136
7.5 Model With Reservoirs That Replicate Virus Frequently 142
7.6 Mutation Model With Latent CD4+ T Reservoirs . 146
7.7 Concluding Remarks 150
PART 3 ADVANCED TOPICS IN CONTROL THEORY
CHAPTER 8 Optimal Therapy Scheduling .155
8.1 Optimal Control Background . 155
8.2 Positive Switched Linear Systems . 159
8.3 Optimal Control for Positive Switched Systems 160
8.4 Optimal Control to Mitigate HIV Escape 163
8.5 Restatement as an Optimization Problem 177
8.6 Dynamic Programming for Positive Switched Systems . 179
8.7 Concluding Remarks 191
CHAPTER 9 Suboptimal Therapy Scheduling .193
9.1 Control of Switched Systems . 193
9.2 Continuous-Time Guaranteed Cost Control . 194
9.3 Discrete-Time Guaranteed Cost Control 196
9.4 Model Predictive Control . 202
9.5 Mitigating HIV Escape Simulations 205
9.6 Concluding Remarks 209
CHAPTER 10 PK/PD-based Impulsive Control .211
10.1 Introduction . 211
10.2 Inverse Optimal Impulsive Control 212
10.3 Tailoring Influenza Treatment 216
10.4 Concluding Remarks 219
Bibliography . 221
Index . 237
Index
A
Abortive cells, 109
Abortive infection, 109,
111
Acute infections, xi, 32, 37,
107, 216
Adaptive cell immune
response, 95
Adaptive immune response,
5, 6, 8, 9, 32,
94
Adaptive immune response
dynamics, 71
Adaptive immune system, 5
Administrated drugs, 217
Adrenal cortical cells, 86
Adult thymus, 109
AIDS, 105, 107–109, 111,
112, 115, 117,
118, 120, 126,
127, 129, 132
in HIV infection, 127
worldwide, 105
Alternating therapy group,
146, 150
Antibody
antigen specificity, 12
response, 13, 95, 98,
100, 102, 103
Anticancer drug, 129
Antigen, 5, 7, 9, 10, 97, 98,
102
EBOV, 97
MHC class, 10
peptide fragments, 7
receptor, 12
Antigenic material
stimulation, 13
Antiretroviral drugs, 129,
131, 134
Antiretroviral drugs for
HIV infection,
129
Antiretroviral therapy, 3,
129, 131, 135,
163
Antiviral
drugs, 216
therapy, 4
therapy combination,
134
Autonomous switched
systems, 161
B
Bacterial
coinfection, 77, 82
infection, 3, 77, 78
outgrowth during
coinfections,
81
Block infection, 135
Blood cells, 131
C
Cells
CTLs, 113
immune, 8, 10, 83, 98
immune system, 9
in HIV infection, 19
in humans, 113
in lymph nodes, 109
Characteristic across
infections, 21
Chronic infections, 32, 33
Chronic viral infection, 32
Clinical
observations, 122, 123,
142, 144, 146
progression, 133
trial SWATCH, 208
Coating antigens, 12
Coinfection, 76–78
Control Lyapunov function
(CLF), 214
Controlling EBOV
infection, 95
Cost function, 36, 39–42,
44, 45, 54,
155, 156, 177,
180, 192, 196,
198, 200, 201
minimization, 42
weighting, 176, 206
Cost functional, 161, 162
optimal, 162, 164, 180
CTLs, 67, 70, 71, 73, 83
cells, 113
homeostatic, 73
proliferation, 73
replenishment, 73
Cytotoxic cells, 10
D
Daughter cells, 10
Dendritic cells (DCs), 6, 7,
86, 97, 107,
109, 113
Differential evolution (DE),
42, 58
Diluted virus suspension,
78
Discontinuous therapy, 133
Disease during coinfection,
80
Disease progression, 107,
109, 111, 127
HIV, 107, 111
DNA viruses, 15
Drug, 133–136, 139, 140,
144, 149, 211,
216, 218
administration, 211
combination, 136, 138,
143, 144
combination
mathematical
model, 137,
148
concentration, 216
development, 211
dynamics, 216
effects, 144
efficiency, 135, 149, 216,
218
intake, 217
phases, 216
projects, 211
resistance, 133, 136
resistance mutations,
134
therapies, 137, 138, 144,
149, 207, 208
treatments, 139
237238 Index
Dynamics, xi, 29, 30, 54,
59, 60, 70–73,
95, 97, 115,
119, 126, 137,
142, 146, 148,
150, 217, 219
antibody, 95, 102
drug, 216
HIV, 137
influenza infection, 67,
70
influenza virus, 37, 67
macrophages, 119, 135
viral, 31, 38, 67, 100,
120, 137, 163
virus, 37, 152, 217
Dynamics modeling, 211
E
Ebola
infection, 85
viral load, 91
virus, 85, 86, 101
virus infection, 86, 87
virus infection
vaccination, 94
EBOV, 85–88, 94, 99, 101
antigen, 97
boosted IgG dynamics,
98
challenge, 96
disease, 85
glycoprotein, 95, 96
infection, 86, 87, 92, 94,
95, 97–99,
101–103
kinetics, 86
natural, 85
prevention, 102
replication, 95–97, 99,
103
replication dynamics, 96
target cells, 87
titers, 96, 102
vaccination, 97, 103
EBOV infection, 95
Eclipse phase, 66, 68, 69,
72
Egg infection, 72
Emerging virus diseases, 3
Endothelial cells, 86
Epithelial cells, 65, 70, 86,
101
Equilibrium point, 27–29,
79, 80, 212
Equine influenza infection,
70
Equine influenza virus, 72
Extracellular antigens, 11
F
Facilitating infection, 109
Filoviridae virus infection,
87
Filovirus virions, 85
Fitness, 17, 72, 139, 143,
144, 148, 149
Fitness genotype, 144, 149
Fitness viral, 72
Flu infection, 31
Foreign antigens, 7
Fusion inhibitors (FI), 129
G
Genotype, 15, 17, 137, 138,
143, 144, 148,
149
fitness, 144, 149
highly resistant, 150
Guaranteed cost, 193, 194,
206–209
Guaranteed cost control,
194, 196, 207
H
HAART, 129, 131–133,
135, 136, 150
experience, 142
in mathematical models,
134
regimens, 132, 134
treatment, 131, 135, 146,
150
Highly resistant genotype
(HRG), 138
HIV, 108
disease progression, 107,
111
drug resistance, 132
dynamics, 137
infection, xi, 15, 17, 105,
110–113,
115–118, 127,
132, 133, 135,
136, 147, 150,
204, 208, 211
in humans, 125
mathematical
modeling, 110
progression, 136
primary infection, 107
reservoirs, 147
RNA, 131–135
RNA levels, 150
therapy, 131
Humans, 3, 85, 95, 101, 216
cells in, 113
immune system, 86
immunodeficiency virus,
105
infected, 14
influenza, 69
I
IgG dynamics, 98
IgG dynamics in EBOV
infection, 97
IgG titers, 96, 98, 101
Immune
cells, 8, 10, 83, 98
response dynamics, 70
responses, 4, 5, 9, 19, 67,
69–71, 73, 85,
95, 101–103
responses dynamics, 98
system, xi, 5–7, 10, 32,
66, 67, 71, 74,
82, 94, 101,
102, 105, 107,
109, 110, 117,
131, 133, 140
cells, 9
components, 82
responses, 15, 65,
110, 114, 124
Impaired immune response,
70
Infected cells, 5, 7, 31, 32,
38, 53, 67,
69–71, 73, 87,
88, 92–94,
113, 114, 125,
135, 148, 208
Infected cells eclipse phase,
72Index 239
Infected humans, 105
Infected macrophages, 10,
112, 113, 115,
119, 124–126,
135, 143, 146
Infection, 7, 14, 15, 31, 32,
53, 55, 65, 68,
70, 72, 78, 79,
85–87, 93–95,
97, 99, 101,
105, 109–111,
114, 115, 117,
118, 120, 124,
125, 127, 131,
133, 150
bacterial, 3, 77, 78
course, 95
cycle, 15
Ebola, 85
EBOV, 86, 87, 92, 94,
95, 97–99,
101–103
HIV, xi, 15, 17, 105,
110–113,
115–118, 127,
132, 133, 135,
136, 147, 150,
204, 208, 211
influenza, 56, 65–67,
69–71, 73,
80–82, 216
influenza virus, 39, 52,
92
kinetics, 53, 91
process, 113
rate, 88, 135, 142, 148
rates for macrophages,
116
viral, 3, 4, 7, 10, 19, 32,
36, 54, 70, 71,
73, 86–88, 91,
219
virus, 38
Infectious diseases, xi, 3, 4,
21, 211
Infectious virus, 15, 111
Infectious virus particles,
114
Influenza, xi, 32, 38, 44, 53,
65–67, 69–71,
73, 74, 76–78,
83, 84
acute symptoms, 65
genetic factors, 82
humans, 69
infection, 56, 65–67,
69–71, 73,
80–82, 216
dynamics, 67, 70
model parameters,
217
infections dissecting, 67
mathematical modeling,
67
pandemic, 76
prophylaxis, 83
specific antibodies, 67
strains, 72
vaccination, 82
vaccines, 67
virus, 72, 88, 91, 94
dynamics, 37, 67
infection, 39, 52, 92
infection dynamics,
82
strains, 83
Influenza A virus (IAV), 53,
65, 78
Initiating antiretroviral
therapy, 132
Initiating therapy, 131
Innate immune responses,
6, 71, 95, 102,
103
Innate immune system, 5,
6, 10, 67
K
Kidney epithelial cells, 87
L
Langerhans cells, 124
Latent infection, 112
Latent reservoirs, 146
Latently infected cells, 110,
148, 150, 208
Least square (LS)
estimation, 58
Lethal
EBOV challenge, 94
viral load, 100
Linear decreasing factors,
144, 149, 150
Linear programming (LP),
182
Linear quadratic (LQ), 156
Lymph nodes, 6, 7, 10, 97,
109, 111, 125
Lymphocyte cell count,
132, 135
M
mAbs dynamics, 95
Macrophages, 6, 8, 9, 86,
97, 106, 107,
110, 112–115,
118, 120, 121,
124–127, 135,
142, 144, 146,
152
dynamics, 119, 135
dynamics in HIV
infection, 121
in HIV infection, 112
infection rate, 113, 122,
126
population, 126
Major histocompatibility
complex
(MHC), 8
Mathematical
model, xi, 19–21, 29, 31,
36–38, 46, 51,
52, 60, 61, 67,
69–73, 81, 83,
84, 86, 102,
110–112, 114,
120, 122, 134,
136–138, 150
model in influenza
infections, 67
model in viral infection,
53
model parameters, 59, 60
modeling, 4, 19, 20, 35,
52, 53, 55, 59,
65, 72, 77, 81,
111, 125, 211
modeling approach, 19,
36, 83
Maximum likelihood
estimation
(MLE), 41, 58
MDCK cells infection, 72240 Index
Minimum cost function,
157
Mitigate viral escape, 165
Model parameters, xi, 20,
35, 36, 38, 44,
49, 52, 53,
57–60, 73, 74,
78
influenza infection, 217
mathematical, 59, 60
Model predictive control
(MPC), 193
Mononuclear phagocytic
cells, 8
Monte Carlo (MC)
simulations,
218
Mouse infection, 77
Multiplicities of infection
(MOI), 87
Mutation rate, 15, 137, 138,
143, 148, 166,
178
N
Natural killer (NK) cells, 6,
10, 67
Nerve cells, 7
Neuraminidase inhibitors,
69, 216
NK cells, 10, 70, 71, 83
Nucleotide reverse
transcriptase
inhibitors
(NRTI), 129,
131
O
Opportunistic infections,
105, 108
Optimal
control, 155–157,
160–164, 166,
171, 174, 177,
182, 185, 186,
191, 193, 202,
203, 205, 206,
208, 209,
212–214
law, 206, 213–215
policies, 176, 212
problem, 155, 156,
161, 164, 166,
171, 177, 191,
193, 209
cost functional, 162,
164, 180
switching signal, 162,
166, 178, 180
Ordinary differential
equations
(ODE), 27, 31
P
Parameter
accuracy, 38, 57, 58, 60
estimation, 4, 20, 35–37,
54, 55, 58, 60,
61, 69
algorithms, 58
problems, 35, 75
procedures, 36, 52, 53
set, 35, 40, 41, 48
Pathogen, 5, 8, 9, 12, 32, 33
Pathogen living, 8
Peptide antigens, 7
Persistent reservoirs, 110
Peyers patches (PP), 10
Phagocytic cells, 97
Pharmacodynamics, 211
Plasma cells, 11
Plasma virus level, 135
Pluripotent hematopoietic
stem cells, 6
Pneumococcal coinfection,
76
Pneumoniae infection, 78,
81
Pontryagin solution, 164,
165, 167, 168,
170, 171, 173,
176
Population dynamics, 21,
69, 114
Population modeling, 21,
29
Postinfluenza susceptibility,
76
Potent antiretroviral drugs,
129
Proactive switching, 134,
151, 165, 177,
185, 186, 192,
207
Productively infected cells,
68, 109
Progression
clinical, 133
HIV infection, 136
of HIV, 120
to AIDS, 111, 112, 117,
118, 126, 127,
129, 133
Protease inhibitors (PI),
129, 131
Protection against
infections, 12
Protective immune
response, 66
Provirus, 106
R
Recombinant vesicular
stomatitis
virus, 96
Recycling therapies, 146
Reemerging viruses, 3
Replication rate, 88, 92,
115, 124,
137–139, 144,
149, 166, 191
Reproductive number, 33,
93
Reproductive number in
EBOV
infection, 93
Reservoirs, 110, 112, 126,
127, 131, 136,
142, 146, 150
dynamics, 126
HIV, 147
viral, 112
Resistant genotypes, 141
Respiratory tract infection,
130
Retrovirus, 106, 125
Reverse transcriptase
inhibitors
(RTI), 135
Ring vaccination trial, 94
RNA viruses, 15, 65, 106
RNA viruses mutation
rates, 17Index 241
Root mean square error
(RMSE), 41,
54
Root mean square (RMS),
41
S
Secondary infection
dynamics, 71
Simulation results, 30, 54,
58, 60, 111,
112, 119, 124,
125, 136, 142,
146, 150, 152,
206, 208, 209
Specialized cells, 6
Stable equilibrium point, 27
Stable infection, 133
Structured treatment
interruptions
(STI), 133
Susceptible cells, 32, 67,
87, 94
Susceptible cells infection
rate, 32, 87, 92
Susceptible population, 33,
94
SWATCH, 146, 151, 207
approach, 142, 146, 150
strategy, 141
therapy, 150
treatment, 141
Switched systems,
159–162, 179,
180, 191, 193,
194
T
T cell receptors (TCR), 9
Target cells, xi, 31, 32, 38,
44, 53, 54, 60,
67, 69–73, 76,
86
EBOV, 87
infection, 72
infection rate, 71
model parameters, 75
Therapy, 4, 19, 132–135,
138–143, 146,
148–150, 176,
177, 186, 191,
207, 208
alternation, 176, 192
combination, 166
for virologic failure, 140
HIV, 131
in HIV infection, 165
sequencing, 134
SWATCH, 150
withdrawal, 133
Therapy failure, 140
Thymic epithelial cells, 109
Thymus, 6, 9, 10, 109–111
Tumor necrosis factor
(TNF), 70
U
UNAIDS estimates, 105
Uncomplicated influenza
infection
dynamics, 69
Uninfected cells, 9, 53, 67
Uninfected macrophages,
112, 119
V
Vaccination, 83, 84, 94, 98,
100–102
EBOV, 97, 103
efficacy, 83
influenza, 82
strategies, 65, 84
Vero cells, 87
Viral
clearance, 44, 53, 54, 66,
69, 75, 88, 89,
99, 138, 166,
176
clearance rate, 76, 137
dynamics, 31, 38, 67,
100, 120, 137,
163
escape, 163, 176, 177,
194, 206, 207,
209
explosion, 118, 124,
126, 207
fitness, 72
genotypes, 137
infection, 3, 4, 7, 10, 19,
32, 36, 54, 70,
71, 73, 86–88,
91, 219
infection dynamics, 69
kinetics, 71, 72, 87, 91
load dynamic, 121
mutation, 15, 136, 140,
167, 169, 179
mutation rates, 15, 138,
176
replication, 13, 15, 44,
53, 59, 70, 88,
94, 97, 99,
100, 105, 106,
111, 113, 115,
124, 129, 131,
134, 135, 139,
146, 148, 150
replication dynamic, 98
reservoirs, 112
strains, 4, 15, 137, 169
titers, 15, 38–40, 67,
70–72, 74, 86,
87, 92, 96,
99–101
Virologic failure, 132–134,
140–142, 144,
146, 150, 165,
176, 207, 208
treatment, 141, 146, 150,
208
Virus, 13–15, 17, 31, 32,
44, 53, 65–67,
71, 73, 74, 78,
85–87, 92–96,
105, 107–113,
117, 120, 125,
131, 136, 146,
217
clearance, 71
dilutions, 15
dynamics, 37, 152, 217
Ebola, 85, 86, 101
eradication, 112
growth, 13, 74
healthy cells infection,
115
infection, 38
infection dynamics in
humans, 101
infectivity, 66
influenza, 72, 88, 91, 94
inoculum, 53
modeling, 36
pandemic, 76242 Index
particle release
dynamics, 72
particles, 13, 31, 32, 87,
105, 114
pathogenic, 65
production, 72, 106
proteins, 14
quantification, 15
replication, 13, 66, 97,
113, 115, 142,
148
strain, 55, 72, 77
surface, 216
titer, 76, 92
W
World Health Organization
(WHO), 3


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