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What factors can help COVID-19 patients to recover quickly in Pakistan

Author

Listed:
  • Irfan, Muhammad
  • Akram, Waqar
  • James Hooper, Vincent

Abstract

The research paper uncovers the socioeconomic factors which are associated with the recovery of coronavirus in Pakistan. Using a self-designed questionnaire, we collected the data from 170 corona recovered patients and applied Multiple linear regression to predict the effect of socioeconomic factors. The paper finds that patients aged between 36 and 46 years take around 3 more days to recover, whereas, patients aged above than 46 years take around 7 more days to recover in comparison to the young adults (17-25 years). In addition, patients who live in urban areas recover almost 1.5 days earlier than those who live in rural areas. Patients who have other serious diseases take almost 1.7 more days to recover in comparison to those who do not have any disease. Patients who smoke take 2 more days to recover in comparison to those who do not smoke. Moreover, obese people take almost 2.6 more days to recover in comparison to those who have a normal body mass index. Surprisingly, income, gender, and education remain insignificant in relation to the recovery days. We suggest, people above 46 should be given extra care, policies regarding smoking cessation, and lowering obesity should be implemented to combat coronavirus.

Suggested Citation

  • Irfan, Muhammad & Akram, Waqar & James Hooper, Vincent, 2020. "What factors can help COVID-19 patients to recover quickly in Pakistan," MPRA Paper 103053, University Library of Munich, Germany, revised 15 Sep 2020.
    • Handle: RePEc:pra:mprapa:103053
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    File URL: https://mpra.ub.uni-muenchen.de/103053/1/MPRA_paper_103053.pdf
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    References listed on IDEAS

    as
    1. Jaber S Alqahtani & Tope Oyelade & Abdulelah M Aldhahir & Saeed M Alghamdi & Mater Almehmadi & Abdullah S Alqahtani & Shumonta Quaderi & Swapna Mandal & John R Hurst, 2020. "Prevalence, Severity and Mortality associated with COPD and Smoking in patients with COVID-19: A Rapid Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-13, May.
    2. Olaniyi Evans, 2020. "Socio-economic impacts of novel coronavirus: The policy solutions," BizEcons Quarterly, Strides Educational Foundation, vol. 7, pages 3-12.
    3. Viktor Stojkoski & Zoran Utkovski & Petar Jolakoski & Dragan Tevdovski & Ljupco Kocarev, 2020. "Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging," Papers 2004.07947, arXiv.org, revised Jan 2022.
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    Keywords

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    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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