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Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

Author

Listed:
  • Mohamed Lamine Sidibé

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Roland Yonaba

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Fowé Tazen

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Héla Karoui

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Ousmane Koanda

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Babacar Lèye

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Harinaivo Anderson Andrianisa

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

  • Harouna Karambiri

    (Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE))

Abstract

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Suggested Citation

  • Mohamed Lamine Sidibé & Roland Yonaba & Fowé Tazen & Héla Karoui & Ousmane Koanda & Babacar Lèye & Harinaivo Anderson Andrianisa & Harouna Karambiri, 2023. "Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 13565-13593, November.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:11:d:10.1007_s10668-022-02646-3
    DOI: 10.1007/s10668-022-02646-3
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    1. Benjamin F. Zaitchik & Neville Sweijd & Joy Shumake-Guillemot & Andy Morse & Chris Gordon & Aileen Marty & Juli Trtanj & Juerg Luterbacher & Joel Botai & Swadhin Behera & Yonglong Lu & Jane Olwoch & K, 2020. "A framework for research linking weather, climate and COVID-19," Nature Communications, Nature, vol. 11(1), pages 1-3, December.
    2. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    3. Anthony Medford & Sergi Trias-Llimós, 2020. "Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(19), pages 533-544.
    4. Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
    5. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    6. Mizanur Rahman & Mahmuda Islam & Mehedi Hasan Shimanto & Jannatul Ferdous & Abdullah Al-Nur Shanto Rahman & Pabitra Singha Sagor & Tahasina Chowdhury, 2021. "A global analysis on the effect of temperature, socio-economic and environmental factors on the spread and mortality rate of the COVID-19 pandemic," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9352-9366, June.
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