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Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Rajan Gupta, Gaurav Pandey, Saibal K. Pal, 2021, original scientific article

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
Published in RUNG: 15.07.2021; Views: 2489; Downloads: 33
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Machine learning models for government to predict COVID-19 outbreak
Rajan Gupta, Gaurav Pandey, Poonam Chaudhary, Saibal K. Pal, 2020, original scientific article

Abstract: The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.
Keywords: COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting
Published in RUNG: 01.04.2021; Views: 2452; Downloads: 85
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Displaying commons and resonances between art and science for a new start from the zero
2020, radio or television broadcast, podcast, interview, press conference

Keywords: sciart, science art, commoning, diversity, artsci, streams, weather forecasting, climate change, communication, curation
Published in RUNG: 16.02.2021; Views: 2447; Downloads: 21
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