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| Título: | Bayesian mechanistic model of COVID-19 transmission dynamics for the estimation of the impact of nonpharmacological measures |
Autor: | Blecua, Javier ; Fernández-Recio, Juan CSIC ORCID | Palabras clave: | COVID-19 modelling Non-pharmacological measures Mechanistic model Bayesian analysis |
Fecha de publicación: | 7-jun-2022 | Citación: | BIFI International Conference: The Science of Covid-19: From molecular drug design to data-driven epidemiological models (2022) | Resumen: | A new mechanistic model that describes the transmission dynamics of COVID-19 was applied to estimate the effect of the non-pharmacological measures by means of Bayesian analysis methods. The consistent results obtained for a total of 32 European countries, with data following very different patterns, confirmed that the model used is an appropriate method for describing the present and future evolution of the disease. | Descripción: | Resumen del trabajo presentado en la BIFI International Conference: The Science of Covid-19: From molecular drug design to data-driven epidemiological models, celebrada en Zaragoza (España), del 7 al 9 de junio de 2022 | URI: | http://hdl.handle.net/10261/303747 |
| Aparece en las colecciones: | (PTI Salud Global) Colección Especial COVID-19 (ICVV) Comunicaciones congresos |
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| Fichero | Descripción | Tamaño | Formato | |
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| Bayesian mechanistic model of COVID-19 transmission dynamics.pdf | 337,86 kB | Adobe PDF |  Visualizar/Abrir |
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