Diffusion on dynamic contact networks with indirect transmission links

Modelling diffusion processes on dynamic contact networks is an important research area for epidemiology, marketing, cybersecurity, and ecology. For these diffusion processes, the interactions among individuals build a transmission network of contagious items that spread out over the contact network. The diffusion dynamics of contagious items on dynamic contact networks are strongly determined by the underlying interaction mechanism between individuals. Thus, various research has been conducted to understand the correlation between diffusion dynamics and interaction patterns. However, current diffusion models only assume that contagious items transmit through interactions where both infected and susceptible individuals are present at a physical space together (e.g. visiting a location at the same time) or active in virtual space at the same time (e.g. friendship in online social networks). The focus on concurrent presence (real or virtual), however, is not sufficiently representative of a class of diffusion scenarios where transmissions can occur with indirect interactions, i.e. where susceptible individuals receive contagious items even if the infected individuals have left the interaction space. For example, an individual infected by the airborne disease can release infectious particles in the air through coughing or sneezing. The particles are then suspended in the air so that a susceptible individual arriving after the departure of the infected individual can still get infected. In this scenario, current diffusion models can miss significant spreading events during delayed indirect interactions. In this thesis, a novel diffusion model called the same place different time transmission based diffusion (SPDT) is introduced to take into account the transmissions through indirect interactions. The behaviour of SPDT diffusion is analysed on real dynamic contact networks and a significant amplification in diffusion dynamics is observed. The SPDT model also introduces some novel behaviours different to current diffusion models. In this work, a new SPDT graph model is also developed to generate synthetic traces to explore SPDT diffusion in several scenarios. The analysis shows that the emergence of new diffusion becomes common thanks to the inclusion of indirect transmissions within the SPDT model. This work finally investigates how diffusion can be controlled and develops new methods to hinder diffusion.This study undertakes infectious diseases spreading as a case study as it captures all aspects of SPDT diffusion processes. The real co-location contact networks constructed by users of a location based social networking application are used for this study. All results are compared with the current diffusion models which are based on direct transmission links.