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Day-Level Forecasting for Coronavirus Disease (COVID-19)

Author

Listed:
  • Wael K. Hanna

    (Sadat Academy for Management Sciences, Egypt)

  • Nouran M. Radwan

    (Sadat Academy for Management Sciences, Egypt)

Abstract

Corona virus (COVID-19) was recently spread quickly all over the world. Most infected people with the Corona virus may experience mild to moderate respiratory illness, but elderly people, and those with chronic diseases are more likely to suffer from serious disease, often leading to death. According to the Egyptian Ministry of Health, there are 96336 confirmed infected cases with Corona virus and 5141 confirmed deaths from the current outbreak. Accurate forecasting of the spread of confirmed and death cases as well as analysis of the number of infected and deaths are crucially required. The present study aims to explore the usage of support vector machine (SVM) in the prediction of coronavirus infected and death cases in Egypt which help in decision-making process. The forecasting model suggest that the number of coronavirus cases grows exponentially in Egypt and more efforts shall be directed to increase the public awareness with this disease. The proposed method is shown to achieve good accuracy and precision results.

Suggested Citation

  • Wael K. Hanna & Nouran M. Radwan, 2021. "Day-Level Forecasting for Coronavirus Disease (COVID-19)," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-16, October.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-16
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    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Timmermann, Allan, 2004. "How costly is it to ignore breaks when forecasting the direction of a time series?," International Journal of Forecasting, Elsevier, vol. 20(3), pages 411-425.
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