Data-driven contact structures: From homogeneous mixing to multilayer networks

PLoS Computational Biology
Alberto AletaYamir Moreno

Abstract

The modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact net...Continue Reading

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Citations

Dec 29, 2020·Proceedings. Mathematical, Physical, and Engineering Sciences·Alina Glaubitz, Feng Fu
Mar 9, 2021·Advances in Difference Equations·Yuyi XueYanni Xiao
Apr 7, 2021·Applied Network Science·Adrià PlazasAlbert Díaz-Guilera
Aug 16, 2021·Epidemiology·Carol Y LiuKristin Nelson

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