期刊
ISA TRANSACTIONS
卷 124, 期 -, 页码 182-190出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2021.01.050
关键词
COVID-19; Epidemic situation analysis; Epidemic forecasting; Evolution algorithm; South Africa
资金
- National Research Foundation of South Africa [113340, 120106]
- UIC Start-up Fund, China [R72021115]
- Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China [19KJB520019]
- Financial Support of Changshu Institute of Technology Start-up Fund, China [XZ1734]
- Natural Science Foundation of Jiangsu Province, China [BK20181033, BK20191029]
- National Natural Science Foundation of China [61901062, 61903050, 62003057]
This paper analyzes and predicts the long-term dynamics of the COVID-19 epidemic in South Africa, revealing the weaknesses of past infection prevention and control measures and providing guidance for future containment strategies. The susceptible-infectious-recovered model is used to analyze the epidemic dynamics and an evolutionary algorithm is utilized for optimal long-term prediction.
The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
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