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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A New Model and Heuristic for Infection Minimization by Cutting Relationships
verfasst von : Rafael de Santiago, Wellington Zunino, Fernando Concatto, Luís C. Lamb
Erschienen in: Neural Information Processing
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Abstract
Models of infection spreading have been used and applied to economic, health, and social contexts. Seeing them as an optimization problem, the spreading can be maximized or minimized. This paper presents a novel optimization problem for infection spreading control applied to networks. It uses as a parameter the number of relations (edges) that must be cut, and the optimal solution is the set of edges that must be cut to ensure the minimal infection over time. The problem uses the states of SEIS nodes, which is based on the SEIR and SIS models. We refer to the problem as Min-SEIS-Cluster. The model also considers that the infections occurred over different probabilities in different clusters of individuals (nodes). We also report a heuristic to solve Min-SEIS-Cluster. The analysis of the obtained results allows one to observe that there exists a positive correlation between the proportion of removed edges and relative increase of mitigation effectiveness.