ArticleApplied soft computing2021
Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Facilitating the spread prediction of public health emergencies based on spatio-temporal neural network.Scientific reports · 2026Article
- The application of the propensity score matching method in stock prediction among stocks within the same industry.PeerJ. Computer science · 2024Article
- Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.Engineering applications of artificial intelligence · 2023Article
- Article
- Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method.Applied soft computing · 2023Article
- Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction.Nonlinear dynamics · 2023Article
- A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.Annals of operations research · 2022Article
- Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.Artificial intelligence in medicine · 2022Article
- Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam.Expert systems with applications · 2022Article
- A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19.Physica A · 2022Article
- Impact of COVID-19 pandemic on the epidemiology of STDs in China: based on the GM (1,1) model.BMC infectious diseases · 2022Article
- Forecasting COInternational journal of environmental research and public health · 2022Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021Article
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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 allocate health resources and formulate epidemic prevention policies effectively.
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