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

Luo, Xilin; Duan, Huiming; Xu, Kai

doi:10.1016/j.chaos.2020.110480

Cited by 36 publications

(20 citation statements)

References 36 publications

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“…The typical SIR model [21], [62] and grey model [61], [63] are adopted as comparison macroscopic-level models. The SIR model is a traditional infectious disease model, and it works with the assumption that the population N PL in the study region is uniform and homogeneously mixed.…”

Section: B Macroscopic-level Modelsmentioning

confidence: 99%

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Cui¹,

Cai²,

Xiao³

et al. 2021

Preprint

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Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

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Section: B Macroscopic-level Modelsmentioning

confidence: 99%

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Cui¹,

Cai²,

Xiao³

et al. 2021

Preprint

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show abstract

“…Şahin and Şahin (2020) [23] , Luo et al. (2020) [24] , and Zhao et al. (2020) [25] studied the prediction of cumulative COVID-19 cases using grey prediction models.…”

Section: Introductionmentioning

confidence: 99%

“…[22] TPF GM (1,1), SARIMA China Şahin and Şahin [23] COVID-19 GM (1,1), NGBM (1,1), and FANGBM (1,1) Italy, UK, and the USA Luo et al. [24] COVID-19 GM (1,1), GVM, ARGM (1,1), ONGM (1,1), ENGM (1,1), ARIMA, NGBM (1,1), GRM (1,1), and GERM (1,1,

) China, Italy, Britain, and Russia Zhao et al. [25] COVID-19 Rolling-GVM China This study COVID-19 GM (1,1), Rolling-GM (1,1), Rolling-PSO-GM (1,1), and NARNN Germany, Turkey, and the USA H1N1: Influenza A Virus Subtype; HBV: Hepatitis B Virus; TPF: Typhoid and Paratyphoid Fevers; DGM: Discrete Grey Model; SMGM(1,1): GM(1,1) model with self-memory principle; GVM: Grey Verhulst Model; NGBM(1,1): Nonlinear Grey Bernoulli Model; PSO-NNGBM(1,1): Nash Nonlinear Grey Bernoulli Model Optimized by Particle Swarm Optimization; HWES: Holt-Winters Exponential Smoothing; PECGM(1,1): Grey-Periodic Extensional Combinatorial Model; FGM(1,1): Modified Grey Model using Fourier Series; ARIMA: Autoregressive Integrated Moving Average; SARIMA: Seasonal Autoregressive Integrated Moving Average; FANGBM(1,1): Fractional Nonlinear Grey Bernoulli Model; ARGM(1,1): Autoregressive Grey Model; ONGM(1,1): Optimized NGM(1,1,k,c) Model; ENGM(1,1): Exact Nonhomogeneous Grey Model; GRM(1,1): Grey Richards Model; GERM(1,1,

): Grey Extend Richards Model; Rolling-GVM: Grey Verhulst Models with a Rolling Mechanism; NARNN: Nonlinear Autoregressive Neural Network; Rolling-GM(1,1): GM(1,1) Model with a Rolling Mechanism; Rolling-PSO-GM(1,1): Grey Modelling (1,1) Optimized by Particle Swarm Optimization with a Rolling Mechanism, COVID-19: Coronavirus Disease 2019.…”

Section: Introductionmentioning

confidence: 99%

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

Ceylan

2021

Applied Soft Computing

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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 Modeling (1,1) is a popular approach used to construct a predictive model with a small-sized data set. 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 Modeling 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 Modeling (1,1) optimized by PSO algorithm performs better than the classical Grey Modeling (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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“…Castillo and Melin 12 proposed a hybrid intelligent fuzzy fractal method for COVID-19 classification of countries. Additionally, Luo et al 13 , Sahin and Sahin 14 , Zhao et al 15 used grey models to study the number of patients infected with COVID-19. The Chaos, Solitons and Fractals launched an open focus issue for understanding and mitigating the effects of the current pandemic 16 .…”

Section: Introductionmentioning

confidence: 99%

A new grey quadratic polynomial model and its application in the COVID-19 in China

Zhang

Jiang

2021

Sci Rep

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This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19.

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

Cited by 36 publications

References 36 publications

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

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

A new grey quadratic polynomial model and its application in the COVID-19 in China

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