20.500.12556/RUNG-6647
Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005
and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
epidemic modeling
machine learning
neural networks
pandemic forecasting
time-series forecasting
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Angleški jezik
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2021-07-15 11:27:19
2021-07-15 14:36:05
2023-06-09 03:44:05
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2021
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str. 69-91
no. 1
Vol. 15
2021
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70396931
616
2470-9379
10.1080/24709360.2021.1913709
URN:SI:UNG:REP:N0TWWPDI
https://doi.org/10.1080/24709360.2021.1913709
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https://repozitorij.ung.si/Dokument.php?lang=slv&id=22484
Univerza v Novi Gorici
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