A computational model of epidemic process with three variants on a synthesized human interaction network

Virus mutations give rise to new variants that cause multiple waves of pandemics and escalate the infected number of individuals. The spread of virus variants on a network could reflect how the disease continues to spread in a human population for a long time and becomes a pandemic. In this paper, we develop a mathematical model by paying attention to the microscopic process of disease spreading in a human interaction network by adding variants in the middle of spreading. We also generate a synthesized human interaction network with a certain structure such as the average number of degrees and the frequency of contacts to describe human interaction in a population. Then, we randomly choose a small number of individuals infected by the original virus at the initial condition and add a small number of individuals infected by other variants after a certain time lag. Unlike the SIR classic models, we do not use a recovery rate in this study. Instead, we use the time when an individual is infected for the first time and the infection period to move an infected individual to a recovered state. Then, we will show several different examples of synthesized human interaction networks to describe how the structures determine epidemic dynamics.