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Having a risky sexual behavior increases the likelihood of infection by the Human Immunodeficiency Virus (HIV), which causes the Acquired Immunodeficiency Syndrome (AIDS). This has been a long lasting problem in high-risk populations such as sex workers: individuals in this population may face drug addiction and share infected needles, or have unprotected sex, and both issues can result in an HIV infection that may then be transmitted to other parts of the population. To study the dynamics of the HIV epidemic in such a high-risk community, we propose a model in which the population is represented as a cellular automaton. At the macro-level, our model accounts for the fact that the sexual behavior of an individual is influenced by the social norms of his acquaintances (social network) as well as by his awareness of HIV status. At the micro-level, randomized neighborhoods provide an explicit representation of personal interactions standing for the large number of non repeated encounters in populations at risk. Our simulations study the dynamics of the disease for different social norms as well as the probability that a seropositive individual get tested.
Evolutionary Computation (CEC), 2011 IEEE Congress on
Date of Conference: 5-8 June 2011