The use of the SIR model to predict the time evolution of an epidemic is very frequent and has spatial information about its propagation which may be very useful to contrast its spread. In this paper we take a particular cellular automaton model that well reproduces the time evolution of the disease given by the SIR model; setting the automaton is generally an annoying problem because we need to run a lot of simulations, compare them to the solution of the SIR model and, finally, decide the parameters to use. In order to make this procedure easier, we will show a fast method that, in input, requires the parameters of the SIR continuous model that we want to reproduce, whereas, in output, it yields the pa- rameters to use in the cellular automaton model. The problem of computing the most suitable parameters for the reticu- lar model is reduced to the problem of finding the roots of a polynomial Equation.

Epidemic propagation. An automaton model as the continuous SIR model

MISICI, Luciano;
2013

Abstract

The use of the SIR model to predict the time evolution of an epidemic is very frequent and has spatial information about its propagation which may be very useful to contrast its spread. In this paper we take a particular cellular automaton model that well reproduces the time evolution of the disease given by the SIR model; setting the automaton is generally an annoying problem because we need to run a lot of simulations, compare them to the solution of the SIR model and, finally, decide the parameters to use. In order to make this procedure easier, we will show a fast method that, in input, requires the parameters of the SIR continuous model that we want to reproduce, whereas, in output, it yields the pa- rameters to use in the cellular automaton model. The problem of computing the most suitable parameters for the reticu- lar model is reduced to the problem of finding the roots of a polynomial Equation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11581/280181
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