A unified framework of mutual influence between two pathogens in multiplex networks

Abstract

There are many evidences to show that different pathogens may interplay each other and cause a variety of mutual influences of epidemics in multiplex networks, but it is still lack of a framework to unify all the different dynamic outcomes of the interactions between the pathogens. We here study this problem and first time present the concept of state-dependent infectious rate, in contrast to the constant infectious rate in previous studies. We consider a model consisting of a two-layered network with one pathogen on the first layer and the other on the second layer, and show that all the different influences between the two pathogens can be given by the different range of parameters in the infectious rates, which includes the cases of mutual enhancement, mutual suppression, and even initial cooperation (suppression) induced final suppression (acceleration). A theoretical analysis is present to explain the numerical results.

Infected or informed? Social structure and the simultaneous transmission of information and infectious disease

“…Theoretical models using multilayer networks have been valuable in understanding the spread of a single parasite species or piece of information through multiple types of interaction, and the consequences of multiple spreading processes occurring across the same set of individuals (for example, multiple information types: Liu et al , multiple parasites: Azimi‐Tafreshi or infection and information together: Funk et al , Funk and Jansen , Marceau et al , Granell et al , , Zhao et al , Guo et al ). Applying these approaches to animal behaviour research (Silk et al , Finn et al ) requires data on multiple types of social connections simultaneously (Franz et al , Gazda et al ), and quantification of the importance of these different social connections for transmission (Aplin et al 2015).…”

Hysteresis loop of nonperiodic outbreaks of recurrent epidemics

“…The spreading of epidemics is currently one of the hottest topics in the field of complex networks, and a great deal of significant progress has been achieved so far, including the infinitesimal threshold [1][2][3][4][5][6], the reactiondiffusion model [7][8][9][10], flow-driven epidemics [11][12][13][14][15], objective spreading [16,17], temporal and/or multilayered networks [18][19][20][21][22][23][24][25][26][27], and other aspects [28][29][30][31][32][33][34][35][36]; see Refs. [37][38][39] for details.…”

Epidemic spreading in complex networks

“…另一种更真实的情况是考虑博弈过程 [95,96] . [107][108][109] , 也可能牵涉 到不同病毒之间的相互耦合 [104,[109][110][111] , 同时还涉及个 体的心理活动和认知 [7] 等. 所以无论是在实证数据分…”