A stochastic Bayesian bootstrapping model for COVID-19 data

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.

Automated summary

This study presents a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain), taking into account the variability in daily reported cases. The framework utilizes generalized logistic growth curves to model the four waves of the pandemic and infers the probability distributions of the input parameters using a Bayesian bootstrap procedure. Results show that this framework provides a more accurate estimation of COVID-19 cases compared to deterministic formulation.