Name: Going Viral: Prediction of Online Virality with Synthedemic Modelling (en Inglés)
Price: 399435.00 COP
Availability: InStock
Author: Nika, Marily
ISBN: 9786202920469

portada Going Viral: Prediction of Online Virality with Synthedemic Modelling (en Inglés)

Formato

Libro Físico

Autor

Marily Nika

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

180

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 1.0 cm

Peso

0.27 kg.

ISBN13

9786202920469

Categorías

Tecnología de la información: cuestiones generales

Going Viral: Prediction of Online Virality with Synthedemic Modelling (en Inglés)

Marily Nika (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Going Viral: Prediction of Online Virality with Synthedemic Modelling (en Inglés)"

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