A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve

Sánchez-Sánchez, Marta; Suárez‐Llorens, Alfonso

doi:10.3390/math9030228

Cited by 17 publications

(10 citation statements)

References 48 publications

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“…The precision of the estimated parameters is determined by the theoretical reasoning of Sections 2.2 and 2.3, and by the good behavior of the model against the observed data. In this sense, some studies [20,21] using Bayesian techniques, show a similar behavior in the estimates.…”

Section: Discussionsupporting

confidence: 52%

“…In addition to the works already considered about SIRD models, interested readers can find a wide variety of models for the study of the COVID-19 pandemic in the scientific literature published in the last months: using Bayesian and stochastic techniques [20][21][22], including mobility [23], confinement and quarantine [15,24], fractional models [25], and logistic models [26], among others.…”

Section: Introductionmentioning

confidence: 99%

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A Modified SIRD Model to Study the Evolution of the COVID-19 Pandemic in Spain

Martínez

2021

Symmetry

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In this paper, we use an SIRD model to analyze the evolution of the COVID-19 pandemic in Spain, caused by a new virus called SARS-CoV-2 from the coronavirus family. This model is governed by a nonlinear system of differential equations that allows us to detect trends in the pandemic and make reliable predictions of the evolution of the infection in the short term. This work shows this evolution of the infection in various changing stages throughout the period of maximum alert in Spain. It also shows a quick adaptation of the parameters that define the disease in several stages. In addition, the model confirms the effectiveness of quarantine to avoid the exponential expansion of the pandemic and reduce the number of deaths. The analysis shows good short-term predictions using the SIRD model, which are useful to influence the evolution of the epidemic and thus carry out actions that help reduce its harmful effects.

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Section: Discussionsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

A Modified SIRD Model to Study the Evolution of the COVID-19 Pandemic in Spain

Martínez

2021

Symmetry

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“…The first is data on Remission times found in Lee and Wang [23] and used by Yahaya and Abba [7] and many other researchers before them. The second data is on COVID-19 Survey in Andalusia, Spain as used by Berihuete et al [24] and can be found here: https://cnecovid.isciii. es/covid19/#documentacin-y-datos and https://www.ine.es/up/9Gq4uzeUiT.…”

Section: Resultsmentioning

confidence: 99%

Bayesian Estimation of the Parameters of the Odd Generalized Exponentiated - Inverse Exponential Distribution (OGE -IED)

Gayus

Doguwa

2022

AJPAS

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The Odd Generalized Exponentiated-Inverse Exponential Distribution, a three parameter distribution, is a hybrid of the Generalized Exponential distribution. Each of the parameters were assigned a gamma prior independently resulting to a posterior distribution that is mathematically intractable impossible to obtain marginal posterior distribution for two of the parameters, and a likelihood function that is not known traditionally to R or other statistical software. Resort was made to STAN in order to obtain Bayesian estimates - leveraging on STAN’s provision for user-defined distribution functions. Two datasets were used; remission times (in months) of bladder cancer patients and COVID-19 Survey data in Andalusia, Spain. In the end, the Maximum Likelihood estimates maximized the likelihood more than the Bayesian estimates - though with a slight margin of not more than 0.77. On the other hand, the Bayesian estimates proved to be more stable yielding very negligible standard errors compared to the Maximum Likelihood estimates.

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“…Bayesian inference has been suggested for other models to accommodate COVID-19 data, such as the Gompertz curve (Berihuete et al. 2021 ). However, the use of the Bayesian bootstrap (Rubin 1981 ) to infer input uncertainties does not seem to have been investigated so far.…”

Section: Introductionmentioning

confidence: 99%

A stochastic Bayesian bootstrapping model for COVID-19 data

Calatayud

Jornet

Mateu

2022

Stoch Environ Res Risk Assess

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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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A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve

Cited by 17 publications

References 48 publications

A Modified SIRD Model to Study the Evolution of the COVID-19 Pandemic in Spain

A Modified SIRD Model to Study the Evolution of the COVID-19 Pandemic in Spain

Bayesian Estimation of the Parameters of the Odd Generalized Exponentiated - Inverse Exponential Distribution (OGE -IED)

A stochastic Bayesian bootstrapping model for COVID-19 data

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