Epidemic spreading and risk perception in multiplex networks: a self-organized percolation method

Abstract

Abstract. The contribution of this paper is twofold. First, we develop a self-organized method which is able the evaluate the percolation threshold of the SIS dynamics in general networks by comparing computational results with theoretical ones. Then, we apply this method to the case of risk perception in which the probability of being infected is reduced by a component which is function of the information about the disease. We then study the interplay between epidemic spreading and risk perception on multiplex networks. The system is represented by two layers: one representing the physical contacts and the other one the virtual contacts in which people exchange information and become aware of the disease. The final contribution of this paper is to comprehend the effectiveness of the source of information in modelling the risk perception in epidemic modelling: we found that the similarity between the physical and the information networks determine the possibility of stopping the infection for a sufficiently high precaution level: if the networks are too different there is no mean of avoiding the epidemics.Résumé. La contribution de cet article est double. Tout d'abord, nous développons une méthode auto-organisée qui est en mesure d'évaluer le seuil de percolation de la dynamique SIS dans les réseaux généraux en confrontant les résultats de simulation aux résultats théoriques. Ensuite, nous appliquons cette méthode pour le cas de la perception du risque dans lequel la probabilité d'infection est réduite d'unélément qui est fonction de l'information sur la maladie. Nousétudions après l'interaction entre la propagation de l'épidémie et la perception du risque sur des réseaux multiplex. Le système est représenté par deux couches: une représentation des contacts physiques et l'autre les contacts virtuels dans lesquels les genséchangent des informations et prennent conscience de la maladie. La contribution finale de ce papier est de permettre la compréhension de l'efficacité de la source de l'information dans la représentation de la perception du risque pour la modélisation de l'épidémie. Nous avons constaté que la similitude entre les réseaux d'information et les réseaux physiques détermine la possibilité d'arrêter l'infection pour un niveau de précaution suffisammentélevé. En d'autres termes, si les réseaux sont trop différents il n'y a pas moyen d'éviter lesépidémies. * FB acknowledges partial financial support from the EU projects 288021 (EINS -Network of Excellence in Internet Science) and 611299 (SciCafe2.0). The authors are grateful to Dr. Nicola Perra for helpful discussions.

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

“…Given the current lack of a consistently used common term for the models we review, it is inevitable that we missed some admissible research. Indeed, it came to our attention that, for instance, [206–208] terms were not retrieved by our search, although they would satisfy our eligibility criteria. This emphasizes the need for a specific terminology.…”

Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data

“…However, most of the network science studies have focused on rather abstract, binary networks, as opposed to those that carry flows, like the real-world systems do [20]. While flow does not necessarily have a physical meaning on certain networks [21], it is conceivable that both the physical topology and flow shall play a role in influencing the function of infrastructure networks [22–25]. However, few existing performance metrics of infrastructure networks account for the topology and flow at the same time.…”

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

“…Furthermore, recently, the interplay between disease and awareness dynamics22232425 has gained a lot of interest, studying how individuals, aware of the potential spread of a certain disease, are able to take preventive measures protecting themselves. In most of these studies, it is explored the interesting interplay between awareness and epidemics when both phenomena compete using different layers of propagation232627282930. Although a lot of works have exploited the framework of multiplex networks and studied the dynamics of the two spreading processes, awareness and disease, none of them has explored the realistic coevolution of the two processes in all the layers of a multiplex network.…”