Perra, Nicola ORCID: https://orcid.org/0000-0002-5559-3064 and Goncalves, Bruno (2015) Modeling and predicting human infectious diseases. Social phenomena: From data analysis to models. Computational Social Sciences, I . Springer International Publishing, Switzerland, pp. 59-83. ISBN 978-3-319-14010-0 (doi:10.1007/978-3-319-14011-7_4)

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Abstract

The spreading of infectious diseases has dramatically shaped our history and society. The quest to understand and prevent their spreading dates more than two centuries. Over the years, advances in Medicine, Biology, Mathematics, Physics, Network Science, Computer Science, and Technology in general contributed to the development of modern epidemiology. In this chapter, we present a summary of different mathematical and computational approaches aimed at describing, modeling, and forecasting the diffusion of viruses. We start from the basic concepts and models in an unstructured population and gradually increase the realism by adding the effects of realistic contact structures within a population as well as the effects of human mobility coupling different subpopulations. Building on these concepts we present two realistic data-driven epidemiological models able to forecast the spreading of infectious diseases at different geographical granularities. We conclude by introducing some recent developments in diseases modeling rooted in the big-data revolution.

Item Type:	Book Section
Additional Information:	© Springer International Publishing Switzerland
Uncontrolled Keywords:	Disease modelling
Faculty / School / Research Centre / Research Group:	Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified:	21 Oct 2020 10:05
URI:	http://gala.gre.ac.uk/id/eprint/13898

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