E-health: agent-based models to simulate behavior of individuals during an epidemic outbreak

Infectious diseases are a threat to human population. Governments around the world invest a lot of money on research of health area. Artificial intelligence techniques are useful to simulate real scenarios and save in public spending. In this paper, the authors present an agent based model of behavior and activities of individuals, according to a set schedule in a population within an urban environment, which is useful for innovation labs. The authors report results on simulations of the AH1N1 influenza epidemic outbreak of 2009 in Toluca, México. Conclusions indicate that the population density implies that a higher concentration of people corresponds to a higher probability of contagion. This parameter is influenced by the activities and interactions that the agents have within the simulation. The proposed model allows a broader perspective to the analysis of infectious disease in a population describing the behavior and interactions among individuals.

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