Data-driven model for the assessment of mycobacterium tuberculosis transmission in evolving demographic structures

Arregui, S. (Universidad de Zaragoza) ; Iglesias, M.J. (Universidad de Zaragoza) ; Samper, S. ; Marinova, D. (Universidad de Zaragoza) ; Martin, C. (Universidad de Zaragoza) ; Sanz, J. ; Moreno, Y. (Universidad de Zaragoza)

Resumen: In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations’ age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age strata that are common to all airborne diseases, but still typically neglected in the TB case. Furthermore, while demographic structures of many countries are rapidly aging, demographic dynamics are pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels predicted for the next decades in the areas of the world that are most hit by the disease today. Similarly, we show that considering realistic patterns of contacts among individuals in different age strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.

Idioma: Inglés

DOI: 10.1073/pnas.1720606115

Año: 2018

Publicado en: Proceedings of the National Academy of Sciences of the United States of America 115, 14 (2018), E3238-E3245
ISSN: 0027-8424

Factor impacto JCR: 9.58 (2018)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 6 / 68 = 0.088 (2018) - Q1 - T1

Factor impacto SCIMAGO: 5.601 - Multidisciplinary (Q1)

Financiación: info:eu-repo/grantAgreement/EC/FP7/317532/EU/Foundational Research on MULTIlevel comPLEX networks and systems/MULTIPLEX
Financiación: info:eu-repo/grantAgreement/EC/H2020/643381/EU/TBVAC2020; Advancing novel and promising TB vaccine candidates from discovery to preclinical and early clinical development/TBVAC2020
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/FIS/PI15-0317
Financiación: info:eu-repo/grantAgreement/ES/MINECO/BIO2014-52580P
Financiación: info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P

Tipo y forma: Article (Published version)

Área (Departamento): Área Microbiología (Dpto. Microb.Med.Pr.,Sal.Públ.)
Área (Departamento): Proy. investigación HQA (Dpto. Microb.Med.Pr.,Sal.Públ.)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)
Área (Departamento): Área Medic.Prevent.Salud Públ. (Dpto. Microb.Med.Pr.,Sal.Públ.)