Abstract
The fast spread of Covid-19 or the novel Coronavirus in the world has influenced it and caused a huge number of deaths. This remains a disastrous warning to general wellbeing and will be set apart as probably the most dangerous pandemic in world history and one of the important health challenges that the world has ever faced. The public health policymakers need the dependable forecasting of the active cases of Covid-19 to plan the future medical facilities. In this work, Machine Learning has been used to forecast the number of active cases of Covid-19 in some countries and in the world using John Hopkins University’s data to track the outbreak, attached by Desktop and Web application using Tkinter and Flask, python’s frameworks for visualizing the data in the affected countries that gives an understandable form of the data powered by different types of charts and choropleth maps and predictions of active cases of Covid-19 which have brought suffering to people everywhere based on two Models (ARIMA and Polynomial Regression).
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Notes
- 1.
“Global virus tracker”, https://globalvirustracker.org/. Accessed 20 Aug 2020.
- 2.
“Novel Coronavirus (COVID-19) Cases Data”, https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases. Accessed 30 Aug 2020.
- 3.
“Alkaline-ml”, https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html. Accessed 10 Sept 2020.
- 4.
“Regression analysis.”https://blog.minitab.com/en/adventures-in-statistics-2/regression-analysis-tutorial-and-examples. Accessed 30 Sept 2020.
- 5.
Regression analysis. https://blog.minitab.com/en/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit. Accessed 30 Sept 2020.
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Neily, N., Ammar, B.B., Kammoun, H.M. (2022). Prediction of COVID-19 Active Cases Using Polynomial Regression and ARIMA Models. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_125
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