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A novel grey model based on traditional Richards model and its application in COVID-19

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  • Luo, Xilin
  • Duan, Huiming
  • Xu, Kai

Abstract

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.

Suggested Citation

  • Luo, Xilin & Duan, Huiming & Xu, Kai, 2021. "A novel grey model based on traditional Richards model and its application in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:chsofr:v:142:y:2021:i:c:s0960077920308729
    DOI: 10.1016/j.chaos.2020.110480
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    References listed on IDEAS

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    Cited by:

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    2. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    3. Conceição Leal & Leonel Morgado & Teresa A. Oliveira, 2023. "Mathematical and Statistical Modelling for Assessing COVID-19 Superspreader Contagion: Analysis of Geographical Heterogeneous Impacts from Public Events," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    4. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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