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. 2022 Feb;16(1):229-238.
doi: 10.1007/s11571-021-09701-1. Epub 2021 Jul 26.

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

Lijun Pei  1 , Mengyu Zhang  1
Affiliations

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

Lijun Pei et al. Cogn Neurodyn. 2022 Feb.

Abstract

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.

Keywords: COVID-19; End time; Long-term prediction; Maximum numbers; Parameter estimation; SIR; SIRD; Turning point.

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Figures

Fig. 1
Fig. 1
Curves of cure and mortality rates of different regions in China
Fig. 2
Fig. 2
Curves of removed rates in Beijing and Zhejiang Province
Fig. 3
Fig. 3
The predictions of numbers of the current infected cases in Wuhan City, Hubei Province, China Mainland and Hubei-Non-Wuhan. Solid curve is the evolution curve of the current infected in model (1), dots represent the true data of the current infected issued by the government. The blue dots stand for data used to estimate the parameters, and the red dots are the real data for the long-term prediction
Fig. 4
Fig. 4
The predictions of numbers of the cumulative death in Wuhan City, Hubei Province, China Mainland and Hubei-Non-Wuhan. Solid curve is the evolution curve of the cumulative death in model (1), and dots are the true data of the death issued by goverment. The blue dots stand for data used to estimate the parameters, and the red dots are the real data for the long-term prediction
Fig. 5
Fig. 5
The prediction numbers of the current infected cases in Beijing, Shanghai, Zhejiang Province and Anhui Province. Solid curve is the evolution curve of the current infected in model (3), dots represent the true data of the current infected issued by the government. The blue dots stand for data used to estimate the parameters, and the red dots are the real data for the long-term prediction
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