Research Article Dynamic Cellular Automata Based Epidemic Spread Model for Population in Patches with Movement Senthil Athithan, 1 Vidya Prasad Shukla, 1 and Sangappa Ramachandra Biradar 2 1 MITS University, Lakshmangarh, Rajasthan 332311, India 2 SDM College of Engineering, Hubli-Dharwad, Karnataka 580002, India Correspondence should be addressed to Senthil Athithan; senthilathithan@hotmail.com Received 19 October 2013; Revised 25 December 2013; Accepted 29 December 2013; Published 12 February 2014 Academic Editor: Arash Massoudieh Copyright © 2014 Senthil Athithan et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Epidemiology is the study of spread of diseases among the group of population. If not controlled properly, the epidemic would cause an enormous number of problems and lead to pandemic situation. Here in this paper we consider the situation of populated areas where people live in patches. A dynamic cellular automata model for population in patches is being proposed in this paper. his work not only explores the computing power of cellular automata in modeling the epidemic spread but also provides the pathway in reduction of computing time when using the dynamic cellular automata model for the patchy population when compared to the static cellular automata which is used for a nonpatchy homogeneous population. he variation of the model with movement of population among the patches is also explored which provides an eicient way for evacuation planning and vaccination of infected areas. 1. Introduction Computation in epidemics helps us to understand various important factors during the epidemic spread. Computation that results in the form of simulated environment gives us the focus on how an evacuation strategy could be planned [1– 3]. Modeling of epidemics using diferential and partial dif- ferential equations has been done by many researchers [4–6] but these approaches were having certain disadvantages like vagueness in handling the boundary conditions, and dynamic nature of the environment was not properly discussed [7]. Cellular automaton model removes this kind of diiculties in modeling the epidemic spread. A substantial number of problems relating to diferential equations have been solved by the cellular automata model which includes Difusion equation [8], Poisson equation [9], Lapalace equation [10], Weyl, Dirac, and Maxwell equations [11]. he cellular automata also proves to be a better choice of usage under speciic boundary and initial conditions in solving the scientiic problems related to partial diferential equations [12]. he nature of cellular automata which brings out the global behavior of the system from the interactions of the local cells makes it a better choice of modeling the epidemic spread than the diferential equations. Modeling epidemic spread using cellular automata has been done by many researchers [13–15]. Sun et al. [16–18] provided breakthrough in understanding the spatial pattern in epidemics and the efect of noise in spatial epidemics. he cellular automaton model for epidemic spread has been given by White et al. [12], in which the rules are assumed to be homogeneous and the population size is also homogeneous. Moreover, the efect of population movement has not been discussed. he cellular automata model with the efects of population movement and vaccination has been given by Sirakoulis et al. [19, 20]. he paper discusses the epidemic propagation during the population movement within the cells but does not specify anything about patches. he paper also discusses the efect of disease spread only but not the mix of susceptible, infective, and recovered accordingly. he main objective of cellular automata models is how to get a global behavior from the local behavior [21–23]. Cellular automata could be static or dynamic in nature as Hindawi Publishing Corporation Journal of Computational Environmental Sciences Volume 2014, Article ID 518053, 8 pages http://dx.doi.org/10.1155/2014/518053