Evidence map›Paper›PMID 35005154›Full record

ArticleData in brief2022

Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset.

Daniel Canton Enriquez, Jose A Niembro-Ceceña, Martin Muñoz Mandujano, Daniel Alarcon, Jorge Arcadia Guerrero, Ivan Gonzalez Garcia, Agueda Areli Montes Gutierrez, Alfonso Gutierrez-Lopez

Abstract read
In one paragraph

Article in Data in brief, 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. Article
  2. 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

8 authors.

Daniel Canton EnriquezFacultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla, Queretaro 76230, Mexico.
Jose A Niembro-CeceñaFacultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla, Queretaro 76230, Mexico.
Martin Muñoz MandujanoFacultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla, Queretaro 76230, Mexico.
Daniel AlarconFacultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla, Queretaro 76230, Mexico.
Jorge Arcadia GuerreroFacultad de Ingeniería, Universidad Autonoma de Queretaro, Centro Universitario, Queretaro 76010, Mexico.
Ivan Gonzalez GarciaFacultad de Informatica, Universidad Autonoma de Queretaro, Juriquilla, Queretaro 76230, Mexico.
Agueda Areli Montes GutierrezFacultad de Ingeniería, Universidad Autonoma de Queretaro, Centro Universitario, Queretaro 76010, Mexico.
Alfonso Gutierrez-LopezWater Research Center, Centro de Investigaciones del Agua-Queretaro (CIAQ), International Flood Initiative, Latin-American and the Caribbean Region (IFI-LAC), Intergovernmental Hydrological Programme (IHP-UNESCO), Universidad Autonoma de Queretaro, Queretaro 76010, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Worldwide, COVID-19 coronavirus disease is spreading rapidly in a second and third wave of infections. In this context of increasing infections, it is critical to know the probability of a specific number of cases being reported. We collated data on new daily confirmed cases of COVID-19 breakouts in: Argentina, Brazil, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Mexico, Poland, Russia, Spain, U.K., and the United States, from the 20th of January, 2020 to 28th of August 2021. A selected sample of almost ten thousand data is used to validate the proposed models. Generalized Extreme-Value Distribution Type 1-Gumbel and Exponential (1, 2 parameters) models were introduced to analyze the probability of new daily confirmed cases. The data presented in this document for each country provide the daily probability of rate incidence. In addition, the frequencies of historical events expressed as a return period in days of the complete data set is provided.

Indexed as

CoronavirusDaily new cases statistical analysisExponential distributionGumbel distributionProbabilistic analysis

Identifiers

PMID35005154
PMCPMC8719919

What OpenQuestion holds

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Registered trials

None linked

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.