Nonlinear dynamic model for COVID-19 epidemics using the Gaussian distributed wiring small-world network technique

Abstract

The coronavirus (COVID-19) pandemic began in late 2019, unleashing a severe human health crisis that lasted three years and caused immeasurable economic losses worldwide. Although the World Health Organization declared the end of its emergency phase in May 2023, formulating measures by predicting transmission trends of the epidemic is one of the crucial ways to monitor and contain this disease. On the other hand, epidemiological studies have shown that the spatial structure of the population is a vital factor in the spread of epidemics. When infectious diseases invade a region, epidemics are often transmitted through connectivity between hosts. Therefore, in this study, we propose a small-world network model to simulate the transmission of the COVID-19 epidemic. However, the small-world network is organized by Gaussian distributed wiring that satisfies the frequency-distance rule in human mobility theory. Based on its nonlinear dynamic properties, an epidemic prediction model is developed. We performed numerical simulations and evaluated the accuracy of the prediction model. The results show that a high level of accuracy was achieved. The prediction model is examined using the MoH Malaysia COVID-19 dataset. When compared, our model showed lower fitting errors and was closer to the actual curves than the SIR model.

Keywords

• Small-world network
• Network theory (graphs)
• COVID-19
• Epidemic model
• Human mobility