Preface | p. xi |
A brief outline of the book | p. xii |
The bare bones: Basic issues in the simplest context | p. 1 |
The epidemic in a closed population | p. 3 |
The questions (and the underlying assumptions) | p. 3 |
Initial growth | p. 4 |
The final size | p. 14 |
The epidemic in a closed population: summary | p. 28 |
Heterogeneity: The art of averaging | p. 33 |
Differences in infectivity | p. 33 |
Differences in infectivity and susceptibility | p. 39 |
The pitfall of overlooking dependence | p. 41 |
Heterogeneity: a preliminary conclusion | p. 43 |
Stochastic modeling: The impact of chance | p. 45 |
The prototype stochastic epidemic model | p. 46 |
Two special cases | p. 48 |
Initial phase of the stochastic epidemic | p. 51 |
Approximation of the main part of the epidemic | p. 58 |
Approximation of the final size | p. 60 |
The duration of the epidemic | p. 69 |
Stochastic modeling: summary | p. 71 |
Dynamics at the demographic time scale | p. 73 |
Repeated outbreaks versus persistence | p. 73 |
Fluctuations around the endemic steady state | p. 75 |
Vaccination | p. 84 |
Regulation of host populations | p. 87 |
Tools for evolutionary contemplation | p. 91 |
Markov chains: models of infection in the ICU | p. 101 |
Time to extinction and critical community size | p. 107 |
Beyond a single outbreak: summary | p. 124 |
Inference, or how to deduce conclusions from data | p. 127 |
Introduction | p. 127 |
Maximum likelihood estimation | p. 127 |
An example of estimation: the ICU model | p. 130 |
The prototype stochastic epidemic model | p. 134 |
ML-estimation of ¿ and ß in the ICU model | p. 146 |
The challenge of reality: summary | p. 148 |
Structured populations | p. 151 |
The concept of state | p. 153 |
i-states | p. 153 |
p-states | p. 157 |
Recapitulation, problem formulation and outlook | p. 159 |
The basic reproduction number | p. 161 |
The definition of R_{0} | p. 161 |
NGM for compartmental systems | p. 166 |
General h-state | p. 173 |
Conditions that simplify the computation of R_{0} | p. 175 |
Sub-models for the kernel | p. 179 |
Sensitivity analysis of R_{0} | p. 181 |
Extended example: two diseases | p. 183 |
Pair formation models | p. 189 |
Invasion under periodic environmental conditions | p. 192 |
Targeted control | p. 199 |
Summary | p. 203 |
Other indicators of severity | p. 205 |
The probability of a major outbreak | p. 205 |
The intrinsic growth rate | p. 212 |
A brief look at final size and endemic level | p. 219 |
Simplifications under separable mixing | p. 221 |
Age structure | p. 227 |
Demography | p. 227 |
Contacts | p. 228 |
The next-generation operator | p. 229 |
Interval decomposition | p. 232 |
The endemic steady state | p. 233 |
Vaccination | p. 234 |
Spatial spread | p. 239 |
Posing the problem | p. 239 |
Warming up: the linear diffusion equation | p. 240 |
Verbal reflections suggesting robustness | p. 242 |
Linear structured population models | p. 244 |
The nonlinear situation | p. 246 |
Summary: the speed of propagation | p. 248 |
Addendum on local finiteness | p. 249 |
Macroparasites | p. 251 |
Introduction | p. 251 |
Counting parasite load | p. 253 |
The calculation of R_{0} for life cycles | p. 260 |
A 'pathological' model | p. 261 |
What is contact? | p. 265 |
Introduction | p. 265 |
Contact duration | p. 265 |
Consistency conditions | p. 272 |
Effects of subdivision | p. 274 |
Stochastic final size and multi-level mixing | p. 278 |
Network models (an idiosyncratic view) | p. 286 |
A primer on pair approximation | p. 302 |
Case studies on inference | p. 307 |
Estimators of R_{0} derived from mechanistic models | p. 309 |
Introduction | p. 309 |
Final size and age-structured data | p. 311 |
Estimating R_{0} from a transmission experiment | p. 319 |
Estimators based on the intrinsic growth rate | p. 320 |
Data-driven modeling of hospital infections | p. 325 |
Introduction | p. 325 |
The longitudinal surveillance data | p. 326 |
The Markov chain bookkeeping framework | p. 327 |
The forward process | p. 329 |
The backward process | p. 333 |
Looking both ways | p. 334 |
A brief guide to computer intensive statistics | p. 337 |
Inference using simple epidemic models | p. 337 |
Inference using 'complicated' epidemic models | p. 338 |
Bayesian statistics | p. 339 |
Markov chain Monte Carlo methodology | p. 341 |
Large simulation studies | p. 344 |
Elaborations | p. 347 |
Elaborations for Part I | p. 349 |
Elaborations for Chapter 1 | p. 349 |
Elaborations for Chapter 2 | p. 368 |
Elaborations for Chapter 3 | p. 375 |
Elaborations for Chapter 4 | p. 380 |
Elaborations for Chapter 5 | p. 402 |
Elaborations for Part II | p. 407 |
Elaborations for Chapter 7 | p. 407 |
Elaborations for Chapter 8 | p. 432 |
Elaborations for Chapter 9 | p. 445 |
Elaborations for Chapter 10 | p. 451 |
Elaborations for Chapter 11 | p. 455 |
Elaborations for Chapter 12 | p. 465 |
Elaborations for Part III | p. 483 |
Elaborations for Chapter 13 | p. 483 |
Elaborations for Chapter 15 | p. 488 |
Bibliography | p. 491 |
Index | p. 497 |
Table of Contents provided by Ingram. All Rights Reserved. |