ReviewInternational journal of environmental research and public health2020
Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models.
Review in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 37 citations in OpenAlex.
- Methodology for predicting hospital admissions and evaluating recovery rates for coronavirus disease in Japan.PloS one · 2025Article
- Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.Scientific reports · 2024Article
- A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19.Physica A · 2022Article
- Effect evaluation of non-pharmaceutical interventions taken in China to contain the COVID-19 epidemic based on the susceptible-exposed-infected-recovered model.Technological forecasting and social change · 2021Article
- Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization.Applied soft computing · 2021Article
- COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021Article
- A new grey quadratic polynomial model and its application in the COVID-19 in China.Scientific reports · 2021Article
- Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China.BMJ open · 2021Article
- Article
- Forecasting the Trend of COVID-19 Considering the Impacts of Public Health Interventions: An Application of FGM and Buffer Level.Journal of healthcare informatics research · 2021Article
- A novel grey model based on traditional Richards model and its application in COVID-19.Chaos, solitons, and fractals · 2021Article
- Four-dimensional evaluation and forecasting of marine carrying capacity in China: Empirical analysis based on the entropy method and grey Verhulst model.Marine pollution bulletin · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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