Abstract
The object under study is an epidemic spread of a disease through individuals. A stochastic process is first introduced, inspired in classical Susceptible, Infected and Removed (SIR) model. In order to jeopardize the epidemic spread, two different immunization strategies are proposed. A combinatorial optimization problem is further formalized. The goal is to minimize the effect of the disease spread, choosing a correct immunization strategy, subject to a budget constraint. We are witness of a counter-intuitive result: in non-virulent scenarios, it is better to immunize common individuals rather than communicative ones. A discussion is provided, together with open problems and trends for future work.
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Piccini, J., Robledo, F., Romero, P. (2015). Node-Immunization Strategies in a Stochastic Epidemic Model. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_19
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DOI: https://doi.org/10.1007/978-3-319-27926-8_19
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