Abstract

The COVID-19 virus emerged abruptly in early 2020 and disseminated swiftly, resulting in a substantial impact on public health. This paper aims to forecast the evolution of large-scale and sporadic COVID-19 outbreaks, stemming from the original strain, within the context of stringent quarantine measures in China. In order to accomplish our objective, we introduce a time-delay factor into the conventional susceptible-infected-removed/susceptible-infected-recovered-dead (SIR/SIRD) model. In the nonautonomous delayed SIRD model, the finite difference method is employed to determine that the transmission rate in a large-scale epidemic area exhibits an approximately exponential decay, the cure rate demonstrates a linear increase, and the death rate is approximately piecewise constant with a downward trend. We employ an improved delayed SIR model for sporadic epidemic regions characterized by extremely low or nearly zero mortality rates. In these regions, the transmission rate is estimated through a two-stage exponential decay function with variable coefficients, while the rate of removal aligns with the recovery rate in the previously mentioned SIRD model. The results of this study demonstrate a high level of concordance with the actual evolution of COVID-19, and the predictive precision can be consistently maintained within a margin of 3%. From the perspective of our model parameters, it is observed that under strict isolation policies, the transmission rate of COVID-19 in China is relatively low and has been significantly reduced. This suggests that government intervention has had a positive effect on epidemic prevention in the country. Moreover, our model has been successfully utilized to forecast the outbreaks caused by the SARS virus in 2003 and the COVID-19 outbreak induced by the Omicron virus in 2022, showcasing its broad applicability and efficacy. This study enables the prompt implementation of measures and allocation of medical resources in different regions, ultimately contributing to the mitigation of economic and social losses.

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