Analyzing Data on the Spread of COVID-19 using Statistical Tools to Predict the Inflexion Point of the Virus in Italy
DOI:
https://doi.org/10.17010/ijcs/2020/v5/i2-3/152206Keywords:
Coronavirus
, Curve-Fitting, Logistic Functions, Inflection Points, Infection Prediction, Python.Manuscript Received
, April 25, 2020, Revised, May 10, Accepted, May 14, 2020.Abstract
The outbreak of the novel coronavirus COVID-19 had resulted in deaths of over 24,000 people by April 20, 2020. The goal of this paper is to use and apply principles of statistics and machine learning on COVID-19 datasets available online to predict the inflection point of the spread of the virus. The inflection point, for the purpose of this paper, is defined as a point in time in days after the outbreak of the virus at which there is a change in the direction of the rate of spreading of the virus. We are using available libraries to fit the data a logistic function.Downloads
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Published
2020-06-30
How to Cite
Narang, N., & Jangir, M. (2020). Analyzing Data on the Spread of COVID-19 using Statistical Tools to Predict the Inflexion Point of the Virus in Italy. Indian Journal of Computer Science, 5(2&3), 7–9. https://doi.org/10.17010/ijcs/2020/v5/i2-3/152206
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References
C-Y. J. Peng, K. L. Lee, and G. M. Ingersoll, “An introduction to Logistic Regression analysis and reporting,†J. of Educational Res., vol. 96, no. 1, pp. 3-14, 2002. https://dx.doi.org/10.1080/00220670209598786
D. Chakrabarty, “Curve fitting: Step-wise least squares method,†AryaBhatta J. of Mathematics & Informatics, vol. 6, no. 1, pp. 15-25, 2014.
