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Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

Author

Listed:
  • Alaeddine Mihoub
  • Hosni Snoun
  • Moez Krichen

    (REDCAD - Unité de Recherche en développement et contrôle d'applications distribuées - ENIS - École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax)

  • Montassar Kahia
  • Riadh Bel Hadj Salah

Abstract

The new so-called COVID-19 virus is unfortunately founded to be highly transmissible across the globe. In this study, we propose a novel approach for estimating the spread level of the virus for each country for three different dates between April and May 2020. Unlike previous studies, this investigation does not process any historical data of spread but rather relies on the socioeconomic indicators of each country. Actually, more than 1000 socioeconomic indicators and more than 190 countries were processed in this study. Concretely, data preprocessing techniques and feature selection approaches were applied to extract relevant indicators for the classification process. Countries around the globe were assigned to 4 classes of spread. To find the class level of each country, many classifiers were proposed based especially on Support Vectors Machines (SVM), Multi-Layer Perceptrons (MLP) and Random Forests (RF). Obtained results show the relevance of our approach since many classifiers succeeded in capturing the spread level, especially the RF classifier, with an F-measure equal to 93.85% for April 15th, 2020. Moreover, a feature importance study is conducted to deduce the best indicators to build robust spread level classifiers. However, as pointed out in the discussion, classifiers may face some difficulties for future dates since the huge increase of cases and the lack of other relevant factors affecting this widespread.

Suggested Citation

  • Alaeddine Mihoub & Hosni Snoun & Moez Krichen & Montassar Kahia & Riadh Bel Hadj Salah, 2020. "Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques," Post-Print hal-03002886, HAL.
  • Handle: RePEc:hal:journl:hal-03002886
    DOI: 10.1109/SMART-TECH49988.2020.00041
    Note: View the original document on HAL open archive server: https://hal.science/hal-03002886
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    References listed on IDEAS

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    4. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Moslem Soofi & Farid Najafi & Behzad Karami-Matin, 2020. "Using Insights from Behavioral Economics to Mitigate the Spread of COVID-19," Applied Health Economics and Health Policy, Springer, vol. 18(3), pages 345-350, June.
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