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

© 2021 Elsevier B.V. All rights reserved..

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 allocate health resources and formulate epidemic prevention policies effectively.

Media Type:

Electronic Article

Year of Publication:

2021

Publication:

2021

Contained In:

To Main Record - volume:109

Contained In:

Applied soft computing - 109(2021) vom: 01. Sept., Seite 107592

Language:

English

Contributors:

Ceylan, Zeynep [Author]

Links:

Volltext

Keywords:

COVID-19
Grey modelling (1,1)
Journal Article
NARNN
Particle swarm optimization
Prediction
Rolling mechanism

Notes:

Date Revised 21.12.2022

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.asoc.2021.107592

funding:

Supporting institution / Project title:

PPN (Catalogue-ID):

NLM326786309