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A stochastic Bayesian bootstrapping model for COVID-19 data.

Julia Calatayud1, Marc Jornet2, Jorge Mateu1

  • 1Departament de Matemàtiques, Universitat Jaume I, 12071 Castellón, Spain.

Stochastic Environmental Research and Risk Assessment : Research Journal
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a stochastic model to accurately estimate COVID-19 cases in Castilla-Leon, Spain, by analyzing four distinct epidemic waves using generalized logistic curves and Bayesian methods.

Keywords:
Bayesian bootstrapCOVID-19 reported infections and wavesDeterministic and stochastic modelingLeast-squares fittingMultiple generalized logistic growth curvesRandom parameters and errors

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Biostatistics

Background:

  • The COVID-19 pandemic presented significant challenges in accurately tracking and predicting disease incidence.
  • Understanding epidemic dynamics is crucial for effective public health interventions.

Purpose of the Study:

  • To develop and validate a stochastic modeling framework for estimating COVID-19 incidence in Castilla-Leon, Spain.
  • To model the four distinct waves of the pandemic observed between March 2020 and February 2021.

Main Methods:

  • Utilized a sum of four generalized logistic growth curves to model each COVID-19 wave.
  • Employed a least-squares optimization procedure to fit twenty input parameters.
  • Applied a Bayesian bootstrap procedure to infer probability distributions for parameters and errors, accounting for daily case variability.

Main Results:

  • The proposed stochastic framework provides a more accurate estimation of reported COVID-19 cases compared to deterministic models.
  • The model successfully described the four distinct epidemic waves observed in the study period.
  • Identified and quantified the variability in daily reported cases through probabilistic parameter estimation.

Conclusions:

  • Stochastic modeling offers a robust approach for understanding and predicting infectious disease dynamics, such as COVID-19.
  • The generalized logistic growth curve is effective in modeling individual epidemic waves.
  • Accurate estimation of COVID-19 incidence is enhanced by incorporating parameter uncertainty within a probabilistic framework.