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.
Highlights
An epidemic of the novel coronavirus disease 2019 (COVID-19) broke out in Wuhan City, Hubei Province, China, in early 2020, and spread rapidly in China and across the world, causing tens of thousands of people to be infected with the virus
The results demonstrate that prediction accuracy of the optimized nonlinear metabolism model based on a rolling mechanism is higher
Parameters of the rolling grey Verhulst model and its derived model were obtained with different rolling sequence lengths, and predicted values were calculated based on recurrence prediction formula
Summary
An epidemic of the novel coronavirus disease 2019 (COVID-19) broke out in Wuhan City, Hubei Province, China, in early 2020, and spread rapidly in China and across the world, causing tens of thousands of people to be infected with the virus. According to the latest data (http://www.nhc.gov.cn/) released by the National Health Commission of the People’s Republic of Considering the limited sample size since the outbreak of the epidemic and that a large sample size is needed to use the classical statistical prediction method, the present research used the grey system theory, which can be modeled with only four data points [1,2] On this basis, a rolling grey Verhulst model and its derived models were established to predict the change trend of the number of cases of COVID-19 infection in China
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