Palestrante
Descrição
Spreading phenomena such as epidemics, rumors and social behavior often interact with each other. In many cases, the interaction is asymmetrical, meaning that one process enhances the spreading of the other, which in turn impairs the first process. Examples of such kind are the interplay between epidemics and awareness (1), digital malicious worms and worm-killer software, fake news and fact-checking websites, among others. There is a considerable amount of research on modeling interacting epidemic-like spreading processes, but most works are focused on the mutually cooperative or competitive cases. In this work, we study and compare different models for two asymmetrically interacting epidemic processes. Our goal is to determine the influence of the relative time scale between the processes, including thus situations in which each process has its own clock, another feature often neglected on the literature. We currently study two models in the asymmetric case: interacting diseases by susceptibility change (2) (model I), and competing strains with superinfection (3) (model II). In both models, each process is an SIS (Susceptible-Infected-Susceptible) disease model, but the interaction scheme is different for each model. We calculate the phase diagrams in a mean field condition (i.e., assuming homogeneous interactions). We show that the phase diagram of model I does not depend on the relative time scale, whereas model II has a critical curve that is affected by the time scale parameter. We also study the prevalence of the impaired disease, whose behavior with the time scale we found to be different for each model. In model I, the prevalence is always higher when the clock of the impaired disease is slower than that of the favored disease, but the exact opposite behavior is observed in model II. We finally show that damped oscillatory behavior can be observed in both models, but it only occurs in specific regions of the phase diagrams; those regions apparently depend on the time scale parameter in both models. With the results obtained so far, we conclude that the relative time scale plays an important role on the interplay between interacting diseases or other spreading phenomena, and that the behavior of the prevalence with the time scale is not the same for different models. Our findings may improve the understanding of asymmetrically interacting spreading processes, and help future works to deal with the question of different time scales.
Referências
1 GRANELL, C.; GÓMEZ, S.; ARENAS, A. Competing spreading processes on multiplex networks: awareness and epidemics. Physical Review E, v. 90, n. 1, p. 012808-1-012808-7, July 2014.
2 SANZ, J. et al. Dynamics of interacting diseases. Physical Review X, v. 4, n. 4, p. 041005-1-041005-22, Oct. 2014.
3 WU, Q.; SMALL, M.; LIU, H. Superinfection behaviors on scale-free networks with competing strains. Journal of Nonlinear Science, v. 23, n. 1, p. 113-127, Feb. 2013.
| Apresentação do trabalho acadêmico para o público geral | Sim |
|---|---|
| Subárea | Sistemas Complexos |
