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

Aleta, A. ; De Arruda, G.F. ; Moreno, Yamir (Universidad de Zaragoza)

Resumen: 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 network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population.

Idioma: Inglés

DOI: 10.1371/journal.pcbi.1008035

Año: 2020

Publicado en: PLoS computational biology 16, 7 (2020), e1008035 1-16
ISSN: 1553-734X

Factor impacto JCR: 4.475 (2020)

Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 8 / 58 = 0.138 (2020) - Q1 - T1
Categ. JCR: BIOCHEMICAL RESEARCH METHODS rank: 16 / 77 = 0.208 (2020) - Q1 - T1

Factor impacto SCIMAGO: 2.628 - Cellular and Molecular Neuroscience (Q1) - Computational Theory and Mathematics (Q1) - Ecology (Q1) - Molecular Biology (Q1) - Genetics (Q1) - Modeling and Simulation (Q1) - Ecology, Evolution, Behavior and Systematics (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E36-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Teórica (Dpto. Física Teórica)