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The Use of Decision Trees for Analysis of the Potential Determinants for the Incidence of Deaths and Cases of Coronavirus (Covid-19) in Different Countries

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  • Joanna Wyrobek

Abstract

Purpose: The objective of this paper is to identify the key economic factors determining the intensity of COVID virus infections and deaths in various countries. Design/Methodology/Approach: The publication uses the methods of k-means clustering, k-nearest neighbors algorithm, DBSCan algorithm to divide countries into different groups in terms of the level of disease and death from COVID. The decision tree analysis provided potential determinants for the severity of the pandemic in different regions of the world. We analyzed 211 countries. As potential determinants, we examined the following factors: average temperature, average precipitation, GDP per capita, population density (in 2018), hospital beds per 1.000 citizens, doctors per 1.000 citizens, share of people aged 65 years or above, pollution (based on the PM2.5 indicator from The World Bank), total tests per 1.000 citizens and health expenditure as a percentage of GDP. Findings: Our analysis revealed that the COVID pandemic intensity in analyzed countries depends on the number of doctors, population density, average temperature, total tests per 1.000 citizens, and GDP per capita by using data from the World Bank. Practical Implications: Research suggests that the efficient healthcare system supports immunological response of population to the COVID virus. Another critical factor is the density of population. These two factors proved to play a critical role in determining the level of the COVID cases in deaths in various countries. Nevertheless, countries with the lowest GDP per capita had very low levels of COVID cases, which suggests that either they do not recognize COVID patients correctly or they will experience the pandemic with time delay. Originality/value: Economic factors proved to be good predictors of the COVID virus development in the analyzed countries, however, contrary to expectations, countries with low GDP per capita so far suffered the least from the COVID virus pandemic.

Suggested Citation

  • Joanna Wyrobek, 2020. "The Use of Decision Trees for Analysis of the Potential Determinants for the Incidence of Deaths and Cases of Coronavirus (Covid-19) in Different Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 556-566.
    • Handle: RePEc:ers:journl:v:xxiii:y:2020:i:special3:p:556-566
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    References listed on IDEAS

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    1. Jérôme Adda, 2016. "Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 891-941.
    2. Pichler, Stefan & Ziebarth, Nicolas R., 2017. "The pros and cons of sick pay schemes: Testing for contagious presenteeism and noncontagious absenteeism behavior," Journal of Public Economics, Elsevier, vol. 156(C), pages 14-33.
    3. Milusheva, Sveta, 2020. "Managing the spread of disease with mobile phone data," Journal of Development Economics, Elsevier, vol. 147(C).
    4. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    5. Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Goutte, Stéphane & Péran, Thomas & Porcher, Thomas, 2020. "The role of economic structural factors in determining pandemic mortality rates: Evidence from the COVID-19 outbreak in France," Research in International Business and Finance, Elsevier, vol. 54(C).
    7. Barmby, Tim & Larguem, Makram, 2009. "Coughs and sneezes spread diseases: An empirical study of absenteeism and infectious illness," Journal of Health Economics, Elsevier, vol. 28(5), pages 1012-1017, September.
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    More about this item

    Keywords

    COVID; data mining; economic determinants.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises

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