Beyond network structure: How heterogenous susceptibility modulates the spread of epidemics

Abstract

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that--for the SIS model--differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.

13 pages, 2 figures

References in corpus (8)

• Statistical mechanics of complex networks
• Epidemic spreading in scale-free networks
• Mixing patterns in networks
• High-resolution measurements of face-to-face contact patterns in a primary school
• Epidemic spreading in correlated complex networks
• Understanding the spreading patterns of mobile phone viruses
• Nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networks
• Langevin approach for the dynamics of the contact process on annealed scale-free networks