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EpiCASim: an automata network simulator applied to the spread of epidemics

spread models are devised for a number of reasons. Epidemiologists want to create simple models where they can test the impact of specific parameters on an epidemic's overall behavior. Computer scientists however, might want to create a software package that accurately depicts the behavior of epidemics as seen in nature. The latter is the motivation for this work. The simulation model we present is based on a generalized probabilistic cellular automata. It permits to reproduce explicitly the interplay of two types of transmission mechanism: the local and the global influences, which in turn can be tuned out. Thus, the probability of a susceptible individual become infective is a superposition of: the local mode of transmission that incorporates the individual-based component from the perspective of the susceptible individuals, the actual (physical) contacts that each susceptible experiences; and the global influence due to intrinsic populational mobility, which may be viewed as resulting of a mean-field (discrete) approach, in the sense that the disease transmission to each susceptible individual also depends on the instantaneous total number of infectious individuals in the population. The results obtained from simulations are consistent with real case studies. The software system consists of four modules: the specification, simulation, visualization and analysis of spatial models in the same environment, using a friendly user interface while at the same time providing considerable flexibility. Although the software was designed to run spatial simulation models in general, currently only cellular automata models have been implemented. Strengths and weaknesses of EpiCASim observed in practice show that there is a general need for improvement concerning the clarification of model structure, as well as the support for systematic validation and robustness tests. Regarding its intended application as a decision support system (DSS), modifications and extensions of EpiCASim will focus on its ability to support explorative learning about epidemic systems.