A SEIARQ model combine with Logistic to predict COVID-19 within small-world networks

Abstract

Since the COVID-19 epidemic, mathematical and simulation models have been extensively utilized to forecast the virus's progress. In order to more accurately describe the actual circumstance surrounding the asymptomatic transmission of COVID-19 in urban areas, this research proposes a model called Susceptible-Exposure-Infected-Asymptomatic-Recovered-Quarantine in a small-world network. In addition, we coupled the epidemic model with the Logistic growth model to simplify the process of setting model parameters. The model was assessed through experiments and comparisons. Simulation results were analyzed to explore the main factors affecting the spread of the epidemic, and statistical analysis that was applied to assess the model's accuracy. The results are consistent well with epidemic data from Shanghai, China in 2022. The model can not only replicate the real virus transmission data, but also anticipate the development trend of the epidemic based on available data, so that health policy-makers can better understand the spread of the epidemic.

Keywords

• seairq epidemic model
• small-world networks
• logistic growth model
• covid-19
• prediction