Journal of Life Sciences 10 (2016) 77-84 doi: 10.17265/1934-7391/2016.02.003 Stochastic Lattice gas Cellular Automata Model for Epidemics Ariel Félix Gualtieri and Juan Pedro Hecht Department of Biophysics, Faculty of Dentistry, University of Buenos Aires, Buenos Aires C1122AAH, Argentina Abstract: The aim of this study was to develop and explore a stochastic lattice gas cellular automata (LGCA) model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells was used. A susceptible-infected-recovered (SIR) scheme was represented. Stochasticity was generated by Monte Carlo method. Dynamics of model was explored by numerical simulations. Model achieves to represent the typical SIR prevalence curve. Performed simulations also show how infection, mobility and distribution of infected individuals may influence the dynamics of propagation. This simple theoretical model might be a basis for developing more realistic designs. Key words: Disease spread, people movement, epidemic model, stochastic lattice gas cellular automata. 1. Introduction In the large majority of studies involving population dynamics, such as the spread of epidemics, direct experimentation is often not feasible. That is why formal models can offer a great help, providing a framework for exploring these scenarios. Mathematical models of epidemics are formal designs that capture the dynamic behaviour of spread of infectious diseases [1, 2]. The first step to define an epidemic model is to classify individuals of the population into different categories corresponding to possible states for the disease under study. According to this grouping, parameters are defined to represent the transition of individuals between these categories over a period of time. The aim then is to study the evolution of the system over time [3]. The choice of which states to include in a model depends on the characteristics of the particular disease being investigated and the purpose of the model. Acronyms for epidemic models are often based on the flow patterns between the different states considered [4]. For example, in a Corresponding author: Ariel Félix Gualtieri, Ph.D., research fields: applied mathematics, epidemiology and biostatistics. susceptible-infected-recovered (SIR) type model, individuals can be in one of three states; they are susceptible (S) and can catch the disease, infected (I) and can spread the disease, or recovered and immune (R). The SIR scheme is representative of diseases in which individuals develop immunity, such as influenza [5-7]. The dynamics of current models of epidemics rarely lead to analytical solutions. To achieve this, it would be necessary simplifications to reduce the formal complexity of the models, which would reduce their representativity [8]. Thus, despite its formal nature, the results provided by current epidemiological mathematical models are often analyzed in a qualitative way from numerical computer simulations. The difficulty of including all relevant factors, the imprecise measurement of biological and behavioural variables, and the extreme sensitivity of many non-linear systems to small changes in parameter values are frequently insurmountable obstacles to accurate quantitative prediction [9]. Thus, qualitative results can provide a coherent framework of analysis that is more efficient than simple intuition, helping in the planning of health policies. Both deterministic and stochastic models are used D DAVID PUBLISHING