Controlling epidemic outbreak based on local dynamic infectiousness on complex networks.
Chaos
; 28(12): 123105, 2018 Dec.
Article
em En
| MEDLINE
| ID: mdl-30599528
Resources are limited in epidemic containment; how to optimally allocate the limited resources in suppressing the epidemic spreading has been a challenging problem. To find an effective resource allocation strategy, we take the infectiousness of each infected node into consideration. By studying the interplay between the resource allocation and epidemic spreading, we find that the spreading dynamics of epidemic is affected by the preferential resource allocation. There are double phase transitions of the fraction of infected nodes, which are different from the classical epidemic model. More importantly, we find that the preferential resource allocation has double-edged sword effects on the disease spreading. When there is a small transmission rate, the infected fraction at the steady state decreases with the increment of degree of resource allocation preference, which indicates that resources of the healthy nodes should be allocated preferentially to the high infectious nodes to constrain the disease spreading. Moreover, when there is a large transmission rate, the fraction of infected nodes at the steady state increases with the increment of the degree of the preference, but the resource allocation is determined by the stage of epidemic spreading. Namely, in the early stage of the disease spreading, resources should be allocated preferentially to the high infectious nodes similar to the case of a small transmission rate. While after the early stage, resources should be allocated to the low infectious nodes. Based on the findings, we propose a simple resource allocation strategy that can adaptively change with the current fraction of infected nodes and the disease can be suppressed to the most extent under the proposed strategy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Transmissíveis
/
Epidemias
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Chaos
Assunto da revista:
CIENCIA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
China