Evidence map›Paper›PMID 35035283›Full record

ArticleStochastic environmental research and risk assessment : research journal2022

A stochastic Bayesian bootstrapping model for COVID-19 data.

Julia Calatayud, Marc Jornet, Jorge Mateu

Abstract read
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In one paragraph

Article in Stochastic environmental research and risk assessment : research journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023
    Article
  2. Modeling noisy time-series data of crime with stochastic differential equations.Stochastic environmental research and risk assessment : research journal · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Julia CalatayudDepartament de Matemàtiques, Universitat Jaume I, 12071 Castellón, Spain.ORCID 0000-0002-9639-1530
Marc JornetDepartament de Matemàtiques, Universitat de València, 46100 Burjassot, Spain.ORCID 0000-0003-0748-3730
Jorge MateuDepartament de Matemàtiques, Universitat Jaume I, 12071 Castellón, Spain.ORCID 0000-0002-2868-7604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

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

Identifiers

PMID35035283
PMCPMC8749118

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.