Mobility and Epidemic Process in Temporal Networks

Complex networks are distinguished to have a particular structure due to its large dimension and its many interactions, which play an important role in its characteristic. In fact, these networks are the locus of many dynamical phenomena such as birth of community, opinion formation, information diffusion, and rumors or epidemic spreading and so on. User mobility is of critical important when you want to know how disease spreads in network. The synthetic models represents a good alternative to describe how humans move. In this paper, we study the dynamics of epidemic spreading in temporal network generated by synthetic mobility model such as Random Waypoint (RWP), Gauss Markov (GM) and Truncated Levy Walk (TLW). Then, we show how proximity in terms in distance could impact a spreading process like droplet or airborne modes of pathogen spreading. Finally, we will statistically evaluate the number of infected nodes by scaling the spreading rate depending on the proximity between agents.