• DOI:10.1088/1742-5468/2012/07/P07005
• Corpus ID: 2530998

Predicting epidemic outbreak from individual features of the spreaders

@article{Silva2012PredictingEO,
  title={Predicting epidemic outbreak from individual features of the spreaders},
  author={Renato Aparecido Pimentel da Silva and Matheus Palhares Viana and Luciano da Fontoura Costa},
  journal={ArXiv},
  year={2012},
  volume={abs/1202.0024}
}

• Renato Aparecido Pimentel da Silva, M. Viana, L. D. Costa
• Published 31 January 2012
• Mathematics
• ArXiv

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 real-world data. In this paper we explore possible correlations between the heterogeneous spread of an epidemic disease governed by the susceptible–infected–recovered (SIR) model, and several attributes of the originating vertices, considering Erdos–Renyi (ER), Barabasi–Albert (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 inter-connections. 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 k-shell index, however, the correlation depends on the topology considered.