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