Contact networks have been recognized to have a central role in the dynamic behavior of spreading processes. The availability of cost-optimal curing strategies, able to control the epidemic propagation, are of primary importance for the design of efficient treatments reducing the number of infected individuals and the extinction time of the infection. In this paper, we investigate the use of Genetic Algorithms for solving the problem of finding an optimal curing strategy in a network where a virus spreads following the Susceptible-Infected-Susceptible (SIS) epidemic model. Exploiting the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS spreading process, we propose a constrained genetic algorithm which determines specific curing rates to each node composing the network, in order to minimize the total curing cost, while suppressing the epidemic. Experiments on both synthetic and real-world networks show that the approach finds solutions whose curing cost is lower than that obtained by a classical baseline method.
Contact networks have been recognized to have a central role in the dynamic behavior of spreading processes. The availability of cost-optimal curing strategies, able to control the epidemic propagation, are of primary importance for the design of efficient treatments reducing the number of infected individuals and the extinction time of the infection. In this paper, we investigate the use of Genetic Algorithms for solving the problem of finding an optimal curing strategy in a network where a virus spreads following the Susceptible-Infected-Susceptible (SIS) epidemic model. Exploiting the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS spreading process, we propose a constrained genetic algorithm which determines specific curing rates to each node composing the network, in order to minimize the total curing cost, while suppressing the epidemic. Experiments on both synthetic and real-world networks show that the approach finds solutions whose curing cost is lower than that obtained by a classical baseline method.
A genetic algorithm for finding an optimal curing strategy for epidemic spreading in weighted networks
Pizzuti C;Socievole A
2018
Abstract
Contact networks have been recognized to have a central role in the dynamic behavior of spreading processes. The availability of cost-optimal curing strategies, able to control the epidemic propagation, are of primary importance for the design of efficient treatments reducing the number of infected individuals and the extinction time of the infection. In this paper, we investigate the use of Genetic Algorithms for solving the problem of finding an optimal curing strategy in a network where a virus spreads following the Susceptible-Infected-Susceptible (SIS) epidemic model. Exploiting the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS spreading process, we propose a constrained genetic algorithm which determines specific curing rates to each node composing the network, in order to minimize the total curing cost, while suppressing the epidemic. Experiments on both synthetic and real-world networks show that the approach finds solutions whose curing cost is lower than that obtained by a classical baseline method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
