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_81
Snippet: The parameters of several simple mathematical models (i.e., logistic, linear, logarithmic, quadratic, cubic, compound, power and exponential) were fitted using GA, PSO, and GWO. The logistic model outperformed other methods and showed promising results on training for 30 days. Extrapolation of the prediction beyond the original observation range of 30-days should not be expected to be realistic considering the new statistics. The fitted models ge.....
Document: The parameters of several simple mathematical models (i.e., logistic, linear, logarithmic, quadratic, cubic, compound, power and exponential) were fitted using GA, PSO, and GWO. The logistic model outperformed other methods and showed promising results on training for 30 days. Extrapolation of the prediction beyond the original observation range of 30-days should not be expected to be realistic considering the new statistics. The fitted models generally showed low accuracy and also weak generalization ability for the five countries. Although the prediction for China was promising, the model was insufficient for extrapolation, as expected. In turn, the logistic GWO outperformed the PSO and GA and the computational cost for GWO was reported as satisfactory. Consequently, for further assessment of the ML models, the logistic model fitted with GWO was used for comparative analysis.
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