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Gillespie simulation

Wile synchronous simulation is straightforward to understand and code, we can manipulate the mathematical description of the simulation probabilities to generate a much more efficient simulation dynamics, known as stochastic or Gillespie simulation. This is far more efficient, more statistically exact, and is generally the method used where possible.

(This is a chapter from Complex networks, complex processes.)

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Synchronous simulation

The simplest way to simulate an epidemic from a computing perspective is to adopt a discrete time model. We tie this together with our compartmented models from earlier to create a simulator for SIR.

(This is a chapter from Complex networks, complex processes.)

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Epidemic simulation

Before we go any further, let’s pause to think about the general theme of computer simulation. This is a huge topic, but some of the common themes, ideas, and terms are essential to understanding how to simulate an epidemic — and equally importantly, to make some of the decisions that need to be made.

(This is a chapter from Complex networks, complex processes.)

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Coding a compartmented model

Having moved from continuous to discrete views of compartmented disease models, we now need to create code so we can simulate such diseases over a network. There are lots of possible compartmented models, so — rather than develop each from scratch — we make use of a Python library the gives us a framework for doing so. This has the advantages that the code we develop can be run under different simulation regimes if required, and can also be run at scale.

(This is a chapter from Complex networks, complex processes.)

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