Predicting epidemic outbreak from individual features of the spreaders
Abstract
Knowing which individuals can be more efficient in spreading a pathogen throughout a determinate environment is a fundamental question in disease control. Indeed, over recent years the spread of epidemic diseases and its relationship with the topology of the involved system have been a recurrent topic in complex network theory, taking into account both network models and realworld data. In this paper we explore possible correlations between the heterogeneous spread of an epidemic disease governed by the susceptibleinfectedrecovered (SIR) model, and several attributes of the originating vertices, considering ErdösRényi (ER), BarabásiAlbert (BA) and random geometric graphs (RGG), as well as a real case study, the US air transportation network, which comprises the 500 busiest airports in the US along with interconnections. Initially, the heterogeneity of the spreading is achieved by considering the RGG networks, in which we analytically derive an expression for the distribution of the spreading rates among the established contacts, by assuming that such rates decay exponentially with the distance that separates the individuals. Such a distribution is also considered for the ER and BA models, where we observe topological effects on the correlations. In the case of the airport network, the spreading rates are empirically defined, assumed to be directly proportional to the seat availability. Among both the theoretical and real networks considered, we observe a high correlation between the total epidemic prevalence and the degree, as well as the strength and the accessibility of the epidemic sources. For attributes such as the betweenness centrality and the kshell index, however, the correlation depends on the topology considered.
 Publication:

Journal of Statistical Mechanics: Theory and Experiment
 Pub Date:
 July 2012
 DOI:
 10.1088/17425468/2012/07/P07005
 arXiv:
 arXiv:1202.0024
 Bibcode:
 2012JSMTE..07..005A
 Keywords:

 Physics  Physics and Society;
 Computer Science  Social and Information Networks;
 Quantitative Biology  Populations and Evolution
 EPrint:
 10 pages, 6 figures