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A stochastic Bayesian bootstrapping model for COVID-19 data.
Calatayud, Julia; Jornet, Marc; Mateu, Jorge.
  • Calatayud J; Departament de Matemàtiques, Universitat Jaume I, 12071 Castellón, Spain.
  • Jornet M; Departament de Matemàtiques, Universitat de València, 46100 Burjassot, Spain.
  • Mateu J; Departament de Matemàtiques, Universitat Jaume I, 12071 Castellón, Spain.
Stoch Environ Res Risk Assess ; : 1-11, 2022 Jan 11.
Article in English | MEDLINE | ID: covidwho-1941672
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Language: English Journal: Stoch Environ Res Risk Assess Year: 2022 Document Type: Article Affiliation country: S00477-022-02170-w

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Language: English Journal: Stoch Environ Res Risk Assess Year: 2022 Document Type: Article Affiliation country: S00477-022-02170-w