Open Access
Subscription Access
Analyzing Data on the Spread of COVID-19 using Statistical Tools to Predict the Inflexion Point of the Virus in Italy
Subscribe/Renew Journal
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.
Keywords
Coronavirus, Curve-Fitting, Logistic Functions, Inflection Points, Infection Prediction, Python.
Manuscript Received: April 25, 2020; Revised: May 10, 2020; Accepted: May 14, 2020.
User
Subscription
Login to verify subscription
Font Size
Information
- 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.
- https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv
Abstract Views: 291
PDF Views: 0