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

Abstract

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.

Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level

“…For some machine learning models, the number of training samples with only 60 days is small, which cannot reflect the optimal performance of the model ( Zhang and Jiang, 2021 ). To verify whether 60 days are enough for training our model, we randomly select several days (e.g., 1 day, 2 days, …, 59 days) from 60 days as new training sets, and analyze the changes of prediction effects in the same testing set (i.e., the rest 19 days).…”

“…The typical SIR model ( Kermack and Mckendrick, 1927 ; Maier and Brockmann, 2020 ) and grey model ( Luo et al, 2021 ; Zhang and Jiang, 2021 ) 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 in the study region is uniform and homogeneously mixed.…”

“…Thus, when we know the initial , , , and the constant values of and , we can predict the number of infectors in the future, based on which the number of daily new cases are calculated. The grey model, favored for its “simple model, strong adaptability, and easy parameter changes”, has been widely used in the field of infectious diseases ( Zhang and Jiang, 2021 ). Unlike the neural network methods that need a substantial number of datasets for training system parameters, the grey model can predict the trend of the COVID-19 well with limited information.…”

Deathdaily: A Python Package Index for predicting the number of daily COVID-19 deaths

“…In statistics, polynomial regression is a form of regression analysis that models the relationship between an independent variable x and a dependent variable y as an n th degree polynomial in x. Polynomial regression is commonly used to observe trends and tendencies in a variety of applications(Zhang and Jiang 2021 ; Lee et al 2011 ; Liu et al 2019 ; Davies et al 2021 ). In deathdaily.py as shown in Fig.…”

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

“…Different models have been used to predict COVID-19 prevalence and mortality rate in recent studies. For example, multiple linear regression [ 8 ], Artificial Neural Network [ 9 ], multilayer perceptron [ 10 ] grey prediction model [ 11 ], simulation model [ 12 ], Holt model [ 13 ], LSTM model [ 14 ], and support vector regression [ 15 , 16 ]. However, the spread of epidemic disease is random and will be affected by many factors [ 17 , 18 ].…”