Multiplex network modeling for the impact of the opinion and behavior of mask-wearing on the spreading and control of COVID-19

Sanaz Gholizadeh
Wednesday, April 5 at 3:30 pm
Ross N638

We propose a network model to find out how and to what extent the level of people's behavior and opinion toward protection measures will affect the spread and transmission of a disease spreading, such as the SEAIRS model of COVID-19. We have demonstrated how two simultaneous spreading processes, such as disease spreading and behavior-changing spreading, work together and impact each other within the same population group by utilizing a multiplex network. For studying people's attitudes toward behavior changing, we specifically consider the case of mask-wearing protection measures. First, the act of matching attitudes and the influence pressure of other connections (known as threshold effect) plus the fear of the increased number of confirmed infected cases (known as the fear effect) is quantified in our model as behavior changing. These two behavior effects may occur via media, virtual networks, and communication between friends. Then, using Micro Markov Chain modeling, we show all the system's possibilities and transition probabilities. Our results show that different levels of people's adherence to mask-wearing as a protection measure can influence disease spreading and vice-versa. Our research reveals periodic solutions that indicate a reciprocal relationship between disease spread and behavior change.