Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization.

Applied Soft Computing
Zeynep Ceylan

Abstract

The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modelling (1,1) is a popular approach used to construct a predictive model with a small-sized dataset.​ In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied to create short-term estimates of the total number of confirmed COVID-19 cases for three countries, Germany, Turkey, and the USA. A rolling mechanism that updates data in equal dimensions was applied to improve the forecasting accuracy of the models. The PSO algorithm was used to optimize the Grey Modelling parameters (1,1) to provide more robust and efficient solutions with minimum errors. To compare the accuracy of the predictive models, a nonlinear autoregressive neural network (NARNN) was also developed. According to the analysis results, Grey Rolling Modelling (1,1) optimized by PSO algorithm performs better than the classical Grey Modelling (1,1), Grey Rolling Modelling (1,1), and NARNN models for predicting the total number of confirmed COVID-19 cases. The present study can provide an important basis for countries to al...Continue Reading

References

Oct 19, 2010·Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases·Fan DingXiaofeng Ren
Apr 26, 2020·The Science of the Total Environment·Anuradha Tomar, Neeraj Gupta
May 4, 2020·The Science of the Total Environment·Zeynep Ceylan
May 28, 2020·International Journal of Hygiene and Environmental Health·Chong YouXiao-Hua Zhou
Jun 6, 2020·Process Safety and Environmental Protection : Transactions of the Institution of Chemical Engineers, Part B·Amal I Saba, Ammar H Elsheikh
Oct 22, 2020·Travel Medicine and Infectious Disease·Mohsen MalekiKim-Hung Pho

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