Local information based resource allocation model for disease suppressing on complex networks

To control epidemics using the limited resources of healthy individuals, a novel model of resource allocation that considers the local information of both the network structure and the state of disease transmission is proposed in this paper. Through extensive simulation experiments based on the susceptible–infected–susceptible epidemic model on complex networks, we find that when only structural information is considered, the higher the preference of resource allocation to small degree nodes, the more effectively the disease can be controlled. While, when only the information of local susceptible density (LSD) is considered, the results are different for different transmission rates. When transmission rate is small, there is always an optimal parameter, at which the infected density is the minimum. However, when the transmission rate is large, the disease can be better suppressed when resources are allocated preferentially to the infected nodes with larger LSD. At last, when both types of information are considered simultaneously, the results can be described from two aspects. Namely, when small degree nodes have the priority to get the resources, there is always an optimal parameter that can suppress the disease to the most extent. On the contrary, when large degree nodes have the priority to get the resources, there is always a worst parameter, at which there is maximum infected density. The results in this paper is of practical significance in constraining the disease spreading.