COVID-19 Patients Analysis using SuperHeat Map and Bayesian Network to identify Comorbidities Correlations under Different Scenarios

  1. O. Nolasco-Jáuregui
  2. L. A. Quezada-Téllez
  3. E. E. Rodriguez-Torres
  4. G. Fernández-Anaya

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Abstract

Background

Given the exposure risk of comorbidities in Mexican society, the new pandemic involves the highest risk for the population in the history.

Objective

This article presents an analysis of the COVID-19 risk from Mexico’s regions.

Method

The study period runs from April 12 to June 29, 2020 (220,667 patients). The method has a nature applied and according to its level of deepening in the object of study it is framed in a descriptive and explanatory analysis type. The data used here has a quantitative and semi-quantitative characteristic because they are the result of a questionnaire instrument made up of 34 fields and the virus test. The instrument is of a deliberate type. According to the manipulation of the variables, this research is a secondary type of practices, and it has a factual inference from an inductive method because it is emphasizing the concomitant variations for each region of the country.

Results

Region 1 and Region 4 have a higher percentage of hospitalized patients, while Region 2 has a minimum of them. The average age of non-hospitalized patients is around 40 years old, while the hospitalized patients’ age it is close to 55 years. The most sensitive comorbidities in hospitalized patients are three principal: obesity, diabetes mellitus and hypertension. The patients whose needed the mechanical respirator were in ranged from 7.45% to 10.79%.

Conclusions

There is a higher risk of lose their lives in the Region 1 and Region 4 territories than in the Region 2, this information was dictated by the statistical analysis..

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  1. ScreenIT

    SciScore for 10.1101/2021.05.11.21257055: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.05.11.21257055v1 on medRxiv