Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models

Abstract

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

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

“…However, the epidemic model is hypothetical and may be affected by factors such as geography and super communicators [11] . From the above three types of models, it can be seen that the infectious disease model and time series model may need a large amount of data for accurate parameter identification.…”

“…Wang et al. [11] used the rolling Verhulst model to predict the final number of COVID-19 infection cases, and good results were achieved. Şahin and Şahin [30] used fractional Nonlinear Grey Bernoulli model to predict the cumulative number of cases in Italy, the United Kingdom and the United States.…”

Four-dimensional evaluation and forecasting of marine carrying capacity in China: Empirical analysis based on the entropy method and grey Verhulst model

“…However, the grey Verhulst model has a strong ability to predict the process underpinning information in the data (i.e., S-type process) ( Huang et al, 2015 ). For example, oil production prediction ( Ma and Liu, 2018 ), decoupling state prediction of energy power and industrial electrification ( Wang et al, 2020 ), COVID-19 infection number prediction ( Zhao et al, 2020 ), etc. This model has the advantages of reliable theory, simple method and high prediction accuracy ( Wang and Li, 2019 ).…”

“…GM(1,1) model is suitable for sequences with strong exponential laws, but it can only describe the monotonically changing process of data, and the simulation results have low accuracy ( Li et al, 2009 ). The grey Markov prediction model needs to divide the data interval, which is relatively subjective ( Zhao et al, 2020 ). The marine carrying capacity system constructed in this paper is a dynamic system with a certain random fluctuation, which is suitable for the use of grey Verhulst model ( Wang et al, 2020 ).…”

COVID-19: Short term prediction model using daily incidence data

“…The different approaches to modeling and forecasting infectious disease epidemics can be characterized as: 1) mechanistic models based on SEIR (referring to Susceptible, Exposed, Infected, and Recovered states) frameworks [13]; 2) time series prediction models such as ARIMA [14], Grey Model [15], and Markov Chain models [16]; and 3) agent type models (i.e. simulating individual activities for a population) [17].…”