title: Modeling the spread of multiple concurrent contagions on networks.
abstract: Many contagions spread over various types of communication networks and their spreading dynamics have been extensively studied in the literature. Here we propose a general model for the concurrent spread of an arbitrary number of contagions in complex networks. The model is stochastic and runs in discrete time, and includes two widely used mechanisms by which a node can change its state. The first, termed the spontaneous state change mechanism, describes spontaneous transition to another state, while the second, termed the contact-induced state change mechanism, describes acquiring other contagions due to contact with the neighbors. We consider reactive discrete-time spreading processes of multiple concurrent contagions where time steps are of finite size without neglecting the possibility of multiple infecting events in a single time step. An essential element for making the model numerically tractable is the use of an approximation for the probability that a node transits to a specific state given any set of neighboring states. Different transmission probabilities may be present between each pair of states. We also derive corresponding continuous-time equations that are simple and intuitive. The model includes many well-known epidemic and rumor spreading models as a special case and it naturally captures spreading processes in multiplex networks.
authors.affiliation: Macedonian Academy of Sciences and Arts, Skopje, Macedonia.
authors.forename: Angel
authors.initials: A
authors.lastname: Stanoev
authors.affiliation: Macedonian Academy of Sciences and Arts, Skopje, Macedonia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Trpevski
authors.affiliation: Macedonian Academy of Sciences and Arts, Skopje, Macedonia.
authors.forename: Ljupco
authors.initials: L
authors.lastname: Kocarev
mesh_terms: D060085:Coinfection / Q000453:epidemiology / Q000635:transmission*; D006801:Humans; D015233:Models, Statistical; D013269:Stochastic Processes