Going Viral: Prediction of Online Virality with Synthedemic Modelling
The history of epidemics and a framework for modelling and predicting online virality using infectious disease modelling
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The study of epidemiology has been an important area of research for hundreds of years. Nowadays, it is increasingly realised that infectious disease dynamics have applications 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 virality. This book provides the history of epidemic frameworks and explores how classical epidemiological models can be applied to model the Internet-based spreading of YouTube videos and BitTorrent downloads....
The study of epidemiology has been an important area of research for hundreds of years. Nowadays, it is increasingly realised that infectious disease dynamics have applications 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 virality. This book provides the history of epidemic frameworks and explores how classical epidemiological models can be applied to model the Internet-based spreading of YouTube videos and BitTorrent downloads. We investigate the characterisation of parameter uncertainty by applying maximum likelihood-based techniques and we study parameter recoverability from single stochastic simulation trajectories. We propose a multiple-epidemic modelling approach for modelling and predicting virality called "synthedemic" modelling, by regarding data sets as a manifestation of a number of synthesised epidemics. This is the 1st mathematical model that reflects the dynamics of mutually reinforcing or inhibiting epidemics, via the syndemic and counter-syndemic interaction effects in multiple overlapping populations
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