Regression tree modelling to predict total average extra costs in household spending during COVID-19 pandemic

  1. Nesma Lotfy

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Abstract

Background

Prevention of coronavirus (COVID-19) regarding households has many aspects, such as buying mask, hand sanitizer, face shield, and many others. As a result of buying the previous items, the household spending per month will increase during the COVID-19 pandemic period. This study aimed to calculate the average costs of each extra item involved in households spending during COVID-19 pandemic and to predict the total average extra costs spending by households.

Results

Most of the respondents were females (81%) and aged between 30 and 40 (56.3%). About 63.1% of families had the same monthly income while 35.4% had a decrease in monthly income. A significant reduction in days of leaving home before and after COVID-19 pandemic was observed (before; median = 6, after; median = 5, P  =  < 0.001). The extra spending in grocery was the dominated item compared to other items (mean = 707.2 L.E./month, SD = 530.7). Regarding regression tree, the maximum average extra costs due to COVID-19 pandemic were 1386 L.E./month (around 88.56$/month (1$—> 15.65L.E.)) while the minimum average extra costs were 217 L.E./month (around 13.86$/month).

Conclusions

The effect of COVID-19 pandemic in households spending varies largely between households, it depends on what they do to prevent COVID-19.

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  1. Version published to 10.1186/s42269-021-00585-z
  2. ScreenIT

    SciScore for 10.1101/2021.01.15.21249732: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ✥ Data cleaning: After collection data, it is coded and fed to SPSS (statistical package for the social sciences (IBM® SPSS® Statistics version 25.0).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    statistical package for the social sciences
    suggested: None

    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 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.

  3. Version published to 10.1101/2021.01.15.21249732v1 on medRxiv