 ## SIR model with tau leap method (page 204) The overwhelming drawback associated with using the Direct Method of Gillespie is their slow simulation times for large population sizes. Gillespie (2001) has recently proposed minor sacrifices in simulation accuracy in order to obtain substantial gains in simulation speed. The new algorithm is called the “τ -leap method” and may be explained as follows:
1) A time step tau is choosen and is assumed to be fixed throughout the simulation
2) Each time step, the number of times an event occurs is assumed to be Poisson distributed with a mean given by the rates of change.
3) The events are performed, the population variables and the time updated and the process is repeated.
For the SIR model this translates to the following: The approximation comes from assuming that the multiple events that occur during a time-step are independent and do not effect the rates of change. This approximation is most accurate when few events occur in each time-step, but the method is fastest when many events occurs each step -- striking the correct balance needs care.
Note that we are using numbers (X,Y,Z) throughout this chapter for greater clarity.

Parameters
 β is the transmission rate and incorporates the encounter rate between susceptible and infectious individuals together with the probability of transmission. γ is called the removal or recovery rate, though often we are more interested in its reciprocal (1/γ) which determines the average infectious period. μ is the per capita death rate. τ is the time-step that is taken each time, which can potentially contain multiple Poisson distributed events. X(0) is the initial number or density of susceptible individuals. Y(0) is the initial number or density of infectious individuals. N is the population size -- assumed to be constant. We assume Z(0)=N-X(0)-Y(0)
All rates are specified in days.

Requirements.
All parameters must be positive. Remember, X, Y, Z and N all refer to integer numbers.
In addition we need to specify a time-step τ.

Files
Python ProgramMATLAB Code.

 Questions and comments to: M.J.Keeling@warwick.ac.uk or rohani@uga.edu Princeton University Press Our research web pages: Matt Keeling      Pejman Rohani