Original Article

Forecasting the Number of New Coronavirus Infections Using an Improved Grey Prediction Model


Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency.

Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model.

Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.

1. National Health Commission of the Peo-ple's Republic of China (2020). Prevent and control the new coronavirus at full stretch. Available from: http://www.nhc.gov.cn/
2. Deng JL (1982). Control problems of grey systems. Syst Control Lett, 1(5): 288-94.
3. Deng JL (1989). Introduction to Grey Sys-tem Theory. J Grey Syst-UK, 1: 1-24.
4. Liu SF, Dang YG, Fang ZG, et al (2010). Grey system theory and its applications. 5th ed. Beijing: Science Press, 226-7.
5. Bezuglov A, Comert G (2016). Short-term freeway traffic parameter prediction: Ap-plication of grey system theory models. Expert Syst Appl, 62: 284-92.
6. Wang ZX, Li Q, Pei LL (2017). Grey fore-casting method of quarterly hydropower production in China based on a data grouping approach. Appl Math Model, 51: 302–16.
7. Wang ZX, Li Q, Pei LL (2018). A seasonal GM (1,1) model for forecasting the elec-tricity consumption of the primary eco-nomic sectors. Energy, 154: 522-34.
8. Zeng B, Ma X, Zhou M (2020). A new-structure grey Verhulst model for China’s tight gas production forecasting. Appl Soft Comput, 96: 106600.
9. Xu N, Ding S, Gong YD, Bai J (2019). Forecasting Chinese greenhouse gas emissions from energy consumption us-ing a novel grey rolling model. Energy, 175: 218-27.
10. Nguyen NT, Tran TT (2019). Optimizing mathematical parameters of Grey system theory: an empirical forecasting case of Vietnamese tourism. Neural Comput Appl, 31: 1075–89.
11. Zeng B, Liu SF, Bai Y, Zhou M (2020). Grey system modeling technology for early prediction and warning of human diseas-es. Chinese J Manage Sci, 28(1): 144-52.
12. Lin ZS, Zhang QS, Liu H (2012). Parame-ters optimization of GM(1,1) model based on artificial fish swarm algorithm. Grey Syst, 2(2): 166-77.
13. Zeng B, Meng W, Tong MY (2016). A self-adaptive intelligence grey predictive model with alterable structure and its application. Eng Appl Artif Intel, 50: 236-44.
14. Wang JJ, Dang YG, Ye J, Xu N, Wang J (2018). An improved grey prediction model based on matrix representations of the optimized initial value. J Grey Syst-UK, 30(3): 143-56.
15. Ding S, Hipel KW, Dang YG (2018). Fore-casting China's electricity consumption using a new grey prediction model. Ener-gy, 149: 314-28.
16. Ma X, Wu WQ, Zeng B, Wang Y, Wu XX(2020). The conformable fractional grey system model. ISA T, 96: 255-71.
17. Mao SH, Gao MY, Xiao XP, Zhu M (2016). A novel fractional grey system model and its application. Appl Math Model, 40(7-8): 5063-76.
18. Xie NM, Liu SF (2009). Discrete grey fore-casting model and its optimization. Appl Math Model, 33(2): 1173-86.
19. Xiao XP, Yang JW, Mao SH, et al (2017). An improved seasonal rolling grey fore-casting model using a cycle truncation ac-cumulated generating operation for traffic flow. Appl Math Model, 51: 386-404.
20. Ma X, Liu ZB (2017). Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in China. J Comput Appl Math, 324: 17-24.
21. Zeng B, Duan HM, Zhou YF (2019). A new multivariable grey prediction model with structure compatibility. Appl Math Model, 75: 385–97.
22. Zhan LQ, Shi HJ (2013). Methods and model of grey modeling for approxima-tion non-homogenous exponential data. Syst Eng Theor Pr, 33(3): 689-94.
23. Ding S, dang YG, Xu N, Wei L (2017). Modeling and optimizing the grey model NGOM (1,1) for the approximation non-homogenous decreasing series. Con-trol Decis, 32(8): 1457-64.
24. Ma X, Hu YS, Liu ZB (2017). A novel kernel regularized nonhomogeneous grey model and its applications. Commun Nonlinear Sci, 48: 51-62.
25. Zeng B, Liu SF (2011). Direct modeling ap-proach of DGM (1,1) with approximate non-homogeneous exponential sequence. Syst Eng Theor Pr, 31(2): 297-301.
26. Dang YG, Liu Z, Ye J (2017). Direct model-ing method of unbiased non-homogeneous grey prediction model. Control Decis, 32(5): 823-28.
27. Wu LF, Liu SF, Yao LG, Yan SL, Liu DL (2013). Grey system model with the frac-tional order accumulation. Commun Nonlin-ear Sci, 18(7): 1775-85.
28. Li KW, Liu L, Zhai JN, Khoshgoftaar TM, Li TM (2016). The improved grey model based on particle swarm optimization al-gorithm for time series prediction. Eng Appl Artif Intel, 55: 285-91.
29. Wang ZX, Li Q (2019). Modelling the non-linear relationship between CO2 emis-sions and economic growth using a PSO algorithm based grey Verhulst model. J Clean Prod, 207: 214-24.
30. Xu N, Dang YG, Gong YD (2017). Novel grey prediction model with nonlinear op-timized time response method for fore-casting of electricity consumption in Chi-na. Energy, 118: 473-80.
31. Nouiri M, Bekrar A, Jemai A, et al (2018). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf, 29(3): 603-15.
32. Navabi M, Davoodi A, Reyhanoglu M (2019). Optimum fuzzy sliding mode control of fuel sloshing in a spacecraft using PSO algorithm. Acta Astronaut, 167: 331-42.
33. Bo Z, Duan HM, Bai Y, Meng W (2018). Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator. Energy, 151: 238-49.
34. Xie NM, Liu SF (2008). Research on the non-homogenous discrete grey model and its parameter’s properties. J Syst Eng Electron, 30(5): 862-7.
IssueVol 50 No 9 (2021) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v50i9.7057
New coronavirus Forecasting the number of infections Grey prediction model Background value optimization Particle swarm optimization

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Li H, Zeng B, Wang J, Wu H. Forecasting the Number of New Coronavirus Infections Using an Improved Grey Prediction Model. Iran J Public Health. 2021;50(9):1842-1853.