Synthedemic Modelling and Prediction of Internet-based Spreading Phenomena

The study of infectious disease dynamics, termed epidemiology, has been an important area of re-search for hundreds of years. Nowadays, it is increasingly realised that such techniques may have application to epidemics of a socio-technological nature. Indeed, the proliferation of the Internet has created new opportunities to study the mechanisms behind the emergence and dynamic behaviour of online phenomena such as Internet-based popularity outbursts. The contributions of this thesis are threefold. Firstly, we explore how classical epidemiological mod-els can be applied to model the Internet-based spreading of YouTube video views and BitTorrent downloads. We assess the potential for epidemiology to explain such phenomena, by progressively fitting and parameterising mono-epidemic models from a single data trace. We investigate the char-acterisation of parameter uncertainty by applying maximum likelihood-based techniques to obtain isosurfaces for different confidence intervals. We also study parameter recoverability from single stochastic simulation trajectories. Secondly, we propose a novel paradigm for modelling and predicting Internet-based phenomena. This framework is based on the composition of multiple compartmental epidemiological models, each o