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The novel coronavirus (COVID-19) epidemic has brought huge economic losses to the whole world, and seriously threatened human life and health (Ren et al., 2020; Alankar et al., 2022). This new infectious virus differs significantly from the past viruses in transmission features, namely, epidemiology, morbidity, mutation frequency, and basic reproduction number (Chen et al., 2020). COVID-19 patients are infectious even in the incubation period, and many individuals are asymptomatically infected (Tan et al., 2020). With incomplete medical testing conditions, it is difficult to identify asymptomatic patients in the early stages of the epidemic. These virus carriers are very dangerous, as they could spread the virus without control (Tang & He, 2021). The modeling of this infectious disease must consider the impact of patients with asymptomatic infection. The modeling results would provide essential references for the prevention and control of future viruses with similar transmission features.
Since the outbreak of COVID-19, the transmission model has been widely adopted to study the disease (Mathias et al., 2020; Linka et al., 2020; Kaur et al., 2021). Many researchers have proposed new propagation models. Liu et al. (2020) proposed the susceptible-infected-reported-unreported (SIRU) model, which for the first time takes account of the number of unreported symptomatic cases, and predicted the disease trends under different levels of prevention and control measures for public health. Yu et al. (2019) introduced the time lag from virus-susceptible individuals to infected individuals, and came up with the susceptible-vaccinated-exposed-infectious-recovered (SVEIR) model. Ivan et al. (2020) established separate compartments for dead cases, and presented the susceptible-exposed-infected-recovered-died (SEIRD) model. Lu et al. (2021) presented the susceptible-infected-quarantined-recovered (SIQR) model with time delay, and analyzed the existence and stability of the equilibrium point of the model. To adapt machine learning apps for COVID-19 computing, Singh et al. (2021) developed a framework that detects possible workplace violations of COVID rules through smart office surveillance. In addition, some scholars (Dhiman et al., 2021; Chang et al., 2021; Abdel-Basset et al., 2021) studied the spread of COVID-19 and provided new ideas, with the aid of deep learning.
Despite offering many new research methods for virus propagation, the above-mention research fails to take into account all-new features, not to mention making an accurate simulation of COVID-19. This paper fully considers the transmission features of COVID-19, introduces the state of asymptomatic infection, refines latent persons as close contacts in virus transmission, and establishes the susceptible-close contact-asymptomatically infected- infected-removed (SCUIR) infectious disease model based on time-varying features. Drawing on the actual transmission data of COVID-19, the model dynamics were examined through quantitative analysis and numerical simulation. The results reveal the epidemic law of the disease, accurately foretell its changing trend, and facilitate the design of efficient prevention and control strategies. The modeling results would provide essential references for preventing and controlling future viruses with similar transmission features.