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Title:Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Authors:ID Gupta, Rajan (Author)
ID Pandey, Gaurav (Author)
ID Pal, Saibal K. (Author)
Work type:Unknown
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Year of publishing:2021
Number of pages:str. 69-91
Numbering:Vol. 15, no. 1
PID:20.500.12556/RUNG-6647 New window
COBISS.SI-ID:70396931 New window
ISSN on article:2470-9379
DOI:10.1080/24709360.2021.1913709 New window
Publication date in RUNG:15.07.2021
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Record is a part of a journal

Title:Biostatistics & epidemiology
Shortened title:Biostatist. epidemiol.
Publisher:Taylor & Francis Group
COBISS.SI-ID:70394883 New window