1 |
|
Introduction |
|
2 |
|
Simple compartmental models |
|
|
2.1 |
ODE solvers |
|
|
2.2 |
Solving ODEs in Python |
|
|
2.3 |
Phase portraits |
|
|
2.4 |
Setting initial parameters |
|
|
2.5 |
Waning immunity |
|
|
2.6 |
Solving SEIR models |
|
|
2.7 |
Solving SIRC models |
|
|
2.8 |
Solving SIRC models with reduced infectiousness |
|
|
2.9 |
Symbolic computation of R_0 in a complex model |
|
|
2.10 |
Contact tracing data with NetworkX |
|
|
2.11 |
Symbolic determination of the moment-generation function |
|
|
2.12 |
Estimating R_t |
|
|
2.13 |
Multi-parameter estimation with lmfit and Emcee |
|
|
|
Funerary transmission |
|
3 |
|
Host factors |
|
|
3.1 |
Calculating the R_0 of complex stratified models |
|
|
3.2 |
Calculating the mixing matrix from a contact network |
|
|
3.3 |
Age differential SIR model |
|
|
3.4 |
Inference of mixing matrices |
|
|
3.5 |
Subcompartmental models |
|
|
(Fig. 3.2) |
Risk-stratified SIR model and coupling |
|
4 |
|
Host-vector and multi-host interactions |
|
|
4.1 |
Implementing the Ross-MacDonald model |
|
|
4.2 |
Creating streamplots |
|
|
4.3 |
Inferring parameters for a vector-borne disease |
|
|
4.4 |
Managing complex models with structures |
|
|
4.5 |
Time dependence in ODE solvers |
|
5 |
|
Multi-pathogen dynamics |
|
|
5.1 |
Solving multi-pathogen compartmental models with transition matrices |
|
|
5.2 |
Modeling the no-coinfection no-cross immunity interaction |
|
|
|
Complete cross-immunity |
|
|
|
No-coinfection no-cross immunity simplified |
|
6 |
|
Modeling the control of infectious disease |
|
|
6.1 |
Targeted vaccination |
|
|
6.2 |
Solving delay differential equations computationally |
|
|
6.3 |
Modeling the effect of different quarantine regimes |
|
|
6.4 |
Iterative stateful evaluation |
|
7 |
|
Temporal dynamics of epidemics |
|
|
7.1 |
Symbolic identification of equilibria |
|
|
7.2 |
Numeric equilibrium of a SIR model |
|
|
7.3 |
Symbolic equilibrium analysis of SEIR models |
|
|
7.4 |
Time series decomposition |
|
|
7.5 |
Plotting time series decompositions |
|
|
7.6 |
Continuous Wavelet spectral analysis |
|
|
7.7 |
Discrete Lyapunov exponents to estimate chaos |
|
|
|
Birth pulsing |
|
|
|
Sinusoidal temporal forcing |
|
8 |
|
Spatial dynamics of epidemics |
|
|
8.1 |
Simple spatial lattices |
|
|
8.2 |
Indexing and manipulation of multi-dimensional arrays |
|
|
8.3 |
Kernel neighbourhoods |
|
|
8.4 |
A neighbourhood model of influence |
|
|
8.5 |
Minimum-filtered spatial lattice |
|
|
8.6 |
Spatial autocorrelation of COVID-19 |
|
|
8.7 |
Modeling the pandemic that never was |
|
|
8.8 |
Access and distance |
|
|
8.9 |
Placing testing sites in Manhattan |
|
|
8.10 |
Nested interdiction |
|
9 |
|
Agent-based modeling |
|
|
9.1 |
Using Mesa |
|
|
9.2 |
Initialising the model |
|
|
9.3 |
Using enumerations to define states |
|
|
9.4 |
Creating the Agent blueprint |
|
|
9.5 |
Probabilistic steps in ABMs |
|
|
9.6 |
Creating the infectious process |
|
|
9.7 |
Networks in Mesa |
|
|
9.8 |
Activations in Mesa |
|
|
9.9 |
The Model class and parametrising the ABM |
|
|
9.10 |
Collection and export from ABMs |
|
|
9.11 |
Creating seed populations |
|
|
9.12 |
Iterative running of ABMs |
|
|
9.13 |
The q infector |
|
|
9.14 |
An ABM for pure vector-borne disease |
|
|
9.15 |
SI-SIRD epidemic competition |
|
|
9.16 |
Competing pathogens with a modal shift |
|
|
9.17 |
Quarantine modeling |
|
|
9.18 |
Vaccination and peer influence |
|
|
9.19 |
Targeted prophylaxis |
|
|
9.20 |
Modeling anti-vaccine sentiment |
|
|
9.21 |
ABM of treatment effects |
|
|
9.22 |
A spatial graph with movement |
|
|
9.23 |
Homesick random-destination walks |
|
|
|
Healthcare capacity contingent mortality |
|