Author: Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson
Title: COVID-19 Outbreak Prediction with Machine Learning Document date: 2020_4_22
ID: nu0pn2q8_57
Snippet: Tables 13 to 17 present the description and coefficients of the linear, logarithmic, quadratic, cubic, compound, power, exponential and logistic equations estimated by GWO. Tables 13 to 17 also present the RMSE and r-square values for each equation fitted to data for China, Italy, Iran, Germany and USA, respectively. As is clear from Tables 13 to 17, in general, the logistic equation followed by the quadratic and cubic equations provided the smal.....
Document: Tables 13 to 17 present the description and coefficients of the linear, logarithmic, quadratic, cubic, compound, power, exponential and logistic equations estimated by GWO. Tables 13 to 17 also present the RMSE and r-square values for each equation fitted to data for China, Italy, Iran, Germany and USA, respectively. As is clear from Tables 13 to 17, in general, the logistic equation followed by the quadratic and cubic equations provided the smallest RMSE and the largest r-square values for the prediction of COVID-19 outbreak. The claim can also be considered from Figure 7 to 11, which presents the capability and trend of each model derived by GWO in the prediction of COVID-19 cases for China, Italy, Iran, Germany, and the USA, respectively.
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