Computational Epidemiology: Data-Driven Modeling of COVID-19

Foreword
Preface
Acknowledgements
Contents
Part I Mathematical epidemiology
Chapter 1 Introduction to mathematical epidemiology
1.1 A brief history of infectious diseases
1.2 Introduction to epidemiology
1.3 Testing, testing, testing
1.4 The basic reproduction number
1.5 Concept of herd immunity
1.6 Concept of immunization
1.7 Mathematical modeling in epidemiology
1.8 Data-driven modeling in epidemiology
Problems
References
Chapter 2 The classical SIS model
2.1 Introduction of the SIS model
2.2 Analytical solution of the SIS model
2.3 Final size relation of the SIS model
Problems
References
Chapter 3 The classical SIR model
3.1 Introduction of the SIR model
3.2 Growth rate of the SIR model
3.3 Maximum infection of the SIR model
3.4 Final size relation of the SIR model
3.5 The Kermack-McKendrick theory
Problems
References
Chapter 4 The classical SEIR model
4.1 Introduction of the SEIR model
4.2 Growth rate of the SEIR model
4.3 Maximum exposure and infection of the SEIR model
4.4 Final size relation of the SEIR model
Problems
References
Part II Computational epidemiology
Chapter 5 Introduction to computational epidemiology
5.1 Numerical methods for ordinary differential equations
5.2 Explicit time integration
5.3 Implicit time integration
5.4 Comparison of explicit and implicit time integration
Problems
References
Chapter 6 The computational SIR model
6.1 Explicit time integration of the SIR model
6.2 Implicit time integration of the SIR model
6.3 Comparison of explicit and implicit SIR models
6.4 Comparison of SIR and SIS models
6.5 Sensitivity analysis of the SIR model
6.6 Example: Early COVID-19 outbreak in Austria
Problems
References
Chapter 7 The computational SEIR model
7.1 Explicit time integration of the SEIR model
7.2 Implicit time integration of the SEIR model
7.3 Comparison of explicit and implicit SEIR models
7.4 Comparison of SEIR and SIR models
7.5 Sensitivity analysis of the SEIR model
7.6 Maximum exposure and infection of the SEIR model
7.7 Dynamic SEIR model
7.8 Example: Early COVID-19 outbreak dynamics in China
7.9 Example: Early COVID-19 outbreak control in Germany
Problems
References
Chapter 8 The computational SEIIR model
8.1 Introduction of the SEIIR model
8.2 Discrete SEIIR model.
8.3 Similar symptomatic and asymptomatic disease dynamics
8.4 Different symptomatic and asymptomatic disease dynamics
8.5 Example: Early COVID-19 outbreak in the Netherlands
Problems
References
Part III Network epidemiology
Chapter 9 Introduction to network epidemiology
9.1 Numerical methods for partial differential equations
9.2 Network diffusion method
9.3 Finite element method
9.4 Comparison of network diffusion and finite element methods
Problems
References
Chapter 10 The network SEIR model
10.1 Network diffusion method of the SEIR model
10.2 Example: COVID-19 spreading across the United States
10.3 Example: Travel restrictions across the European Union
Problems
References
Part IV Data-driven epidemiology
Chapter 11 Introduction to data-driven epidemiology
11.1 Introduction to data-driven modeling
11.2 Bayes’ theorem for discrete parameter values
11.3 Bayes’ theorem for continuous parameter values
11.4 Data-driven SIS model
Problems
References
Chapter 12 Data-driven dynamic SEIR model
12.1 Introduction of the data-driven dynamic SEIR model
12.2 Example: Inferring reproduction of COVID-19 in Europe
12.3 Example: Correlating mobility and reproduction of COVID-19
Problems
References
Chapter 13 Data-driven dynamic SEIIR model
13.1 Introduction of the data-driven dynamic SEIIR model
13.2 Example:Visualizing asymptomatic COVID-19 transmission
13.3 Example: Asymptomatic COVID-19 transmission worldwide
13.4 Example: Inferring the outbreak date of COVID-19
Problems
References
Chapter 14 Data-driven network SEIR model
14.1 Introduction of the data-driven network SEIR model
14.2 Example: The Newfoundland story
Problems
References
Index