A stochastic Bayesian bootstrapping model for COVID-19 data
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Título
A stochastic Bayesian bootstrapping model for COVID-19 dataFecha de publicación
2022-01-11Editor
SpringerCita bibliográfica
Calatayud, J., Jornet, M. & Mateu, J. A stochastic Bayesian bootstrapping model for COVID-19 data. Stoch Environ Res Risk Assess (2022). https://doi.org/10.1007/s00477-022-02170-wTipo de documento
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Resumen
We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described ... [+]
We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described by a generalized logistic growth curve. Accordingly, the four waves are modeled through a sum of four generalized logistic growth curves. Pointwise values of the twenty input parameters are fitted by a least-squares optimization procedure. Taking into account the significant variability in the daily reported cases, the input parameters and the errors are regarded as random variables on an abstract probability space. Their probability distributions are inferred from a Bayesian bootstrap procedure. This framework is shown to offer a more accurate estimation of the COVID-19 reported cases than the deterministic formulation. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment (2022)Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades, Spain | Generalitat Valenciana | Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033), Spain
Código del proyecto o subvención
ID2019-107392RB-I00 | AICO/2019/198 | PID2020-115270GB-I00
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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info:eu-repo/semantics/openAccess
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