Cellular automata modelling of SEIRS

In this paper the SEIRS epidemic spread is analysed, and a two-dimensional probability cellular automata model for SEIRS is presented. Each cellular automation cell represents a part of the population that may be found in one of five states of individuals: susceptible, exposed (or latency), infected, immunized (or recovered) and death. Here studied are the effects of two cases on the epidemic spread. i.e. the effects of non-segregation and segregation on the latency and the infected of population. The conclusion is reached that the epidemic will persist in the case of non-segregation but it will decrease in the case of segregation. The proposed model can serve as a basis for the development of algorithms to simulate real epidemics based on real data. Last we find the density series of the exposed and the infected will fluctuate near a positive equilibrium point, when the constant for the immunized is less than its corresponding constant \tau_0. Our theoretical results are verified by numerical simulations.