2020/06/30 by Fátima Velásquez-Rojas, Paulo Cesar Ventura, Paulo C. Ventura +5 · 49 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Physics and Astronomy · #Bioinformatics #Biology #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Computer science #Data science #Disease #Disease transmission #Econometrics #Environmental health #Epidemic model #Homogeneous #Information Dissemination #Mathematics #Medicine #Monte Carlo method #Multiplex #Opinion Dynamics and Social Influence #Physics #Population #Process (computing) #Statistical physics #Statistics #Telecommunications #Transmission (telecommunications) #Virology #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.1103/physreve.102.022312
published in Physical review. E 102(2), 022312 (American Physical Society) · 9 pages, 6 figures
arxiv created 2020/08/03 · openalex publication_date 2020/08/24 · arxiv updated 2020/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Nowadays, one of the challenges we face when carrying out modeling of epidemic spreading is to develop methods to control disease transmission. In this article we study how the spreading of knowledge of a disease affects the propagation of that disease in a population of interacting individuals. For that, we analyze the interaction between two different processes on multiplex networks: the propagation of an epidemic using the susceptible-infected-susceptible dynamics and the dissemination of information about the disease-and its prevention methods-using the unaware-aware-unaware dynamics, so that informed individuals are less likely to be infected. Unlike previous related models where disease and information spread at the same time scale, we introduce here a parameter that controls the relative speed between the propagation of the two processes. We study the behavior of this model using a mean-field approach that gives results in good agreement with Monte Carlo simulations on homogeneous complex networks. We find that increasing the rate of information dissemination reduces the disease prevalence, as one may expect. However, increasing the speed of the information process as compared to that of the epidemic process has the counterintuitive effect of increasing the disease prevalence. This result opens an interesting discussion about the effects of information spreading on disease propagation.