Suppressing epidemic spreading by optimizing the allocation of resources between prevention and treatment

• Published in: Chaos (Woodbury, N.Y.) Vol. 29; no. 11; p. 113108
• Main Authors: Li, Jiayang, Yang, Chun, Ma, Xiaotian, Gao, Yachun, Fu, Chuanji, Yang, Hongchun
• Format: Journal Article
• Language: English
• Published: United States 01-11-2019
• Subjects: Epidemics - prevention & control; Humans; Models, Theoretical
• ISSN: 1089-7682
• DOI: 10.1063/1.5114873

The rational allocation of resources is crucial to suppress the outbreak of epidemics. Here, we propose an epidemic spreading model in which resources are used simultaneously to prevent and treat disease. Based on the model, we study the impacts of different resource allocation strategies on epidemic spreading. First, we analytically obtain the epidemic threshold of disease using the recurrent dynamical message passing method. Then, we simulate the spreading of epidemics on the Erdős-Rényi (ER) network and the scale-free network and investigate the infection density of disease as a function of the disease infection rate. We find hysteresis loops in the phase transition of the infection density on both types of networks. Intriguingly, when different resource allocation schemes are adopted, the phase transition on the ER network is always a first-order phase transition, while the phase transition on the scale-free network transforms from a hybrid phase transition to a first-order phase transition. Particularly, through extensive numerical simulations, we find that there is an optimal resource allocation scheme, which can best suppress epidemic spreading. In addition, we find that the degree heterogeneity of the network promotes the spreading of disease. Finally, by comparing theoretical and numerical results on a real-world network, we find that our method can accurately predict the spreading of disease on the real-world network.