[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3500931.3501030acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisaimsConference Proceedingsconference-collections
research-article

Statistical analysis of the effect of aging on the prevalence of Covid-19

Published: 22 December 2021 Publication History

Abstract

COVID-19 is the most severe global epidemic in recent years. Although the risk factors that influence the outbreak are unknown, it is known from the factors influencing past respiratory pandemics that ageing is one of the common factors affecting the spread of the disease. Therefore, this study uses R and SAS software to analyze the collected COVID-19 data from 191 different countries around the world in a progressive linear relationship, so as to explore whether ageing is one of the factors affecting the prevalence of COVID-19 in different countries around the world. According to the results, the factors affecting ageing are discussed, the limitations of the research method are analyzed, and advice on how older people can avoid getting COVID-19 is given.

References

[1]
Xiu, W., Jinting, Z., Exploration of spatial-temporal varying impacts on Covid-19 cumulative case in Texas using geographically weighted regression(GWR), Environmental science and pollution research international, 2021
[2]
Fonseca G A., Normando P G., Loureiro L V M., Rodrigues R E F., Oliveira V A., Melo M D T., Santana I A. Reduction in the number of procedures and hospitalizations and increase in cancer mortality during the Covid-19 pandemic in Brazil
[3]
Chen, X., Liao, B., Cheng, L., Peng, X., Xu, X., & Li, Y. et al. (2020). The microbial coinfection in COVID-19. Applied Microbiology And Biotechnology, 104(18), 7777--7785.
[4]
World Health Organization. (n.d.). Ageing. World Health Organization. https://www.who.int/health-topics/ageing#tab=tab_1.
[5]
Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., Pearson, C. A. B., Quilty, B. J., Kucharski, A. J., Gibbs, H., Clifford, S., Gimma, A., van Zandvoort, K., Munday, J. D., Diamond, C., Edmunds, W. J., Houben, R. M. G. J., Hellewell, J., Russell, T. W., Abbott, S., … Eggo, R. M. (2020). Age-dependent effects in the transmission and control of COVID-19 epidemics. Nature Medicine, 26(8), 1205--1211. https://doi.org/10.1038/s41591-020-0962-9
[6]
Caramelo, F., Ferreira, N., & Oliveiros, B. (2020). Estimation of risk factors for COVID-19 mortality - preliminary results. https://doi.org/10.1101/2020.02.24.20027268.
[7]
World Population Ageing 2019 - United Nations. (n.d.). https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Report.pdf.
[8]
ISO/TC 314 - Ageing societies. ISO. (2021, July 22). https://www.iso.org/committee/6810883.html.
[9]
Smith G, N., Theresa N, N., Praise M, C. SARS-Cov 2 (Covid-19) Heterogeneous mortality rates across countries may be partly explained by life expectancy, calorie intake, and prevalence of diabetes. Human ecology: an interdisciplinary journal, 2020, 1--6
[10]
Swapnil, K., Hei Kit, C., Henry E, W., Justin Xavier, M. Predictors for country level variations in initial 4-week Covid-19 incidence and case fatality risk in the United States. Research square, 2021

Index Terms

  1. Statistical analysis of the effect of aging on the prevalence of Covid-19

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
    October 2021
    593 pages
    ISBN:9781450395588
    DOI:10.1145/3500931
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 December 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. COVID-19
    2. Linear Regression

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ISAIMS 2021

    Acceptance Rates

    Overall Acceptance Rate 53 of 112 submissions, 47%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 22
      Total Downloads
    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 12 Dec 2024

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media