SARS-CoV-2 has spread worldwide and has caused corona-related pandemics since the end of 2019. To deal with these unknown phenomena, it is effective to discuss and apply the results obtained through simulations to policy-making to control epidemics. Several network-based simulations have been developed in previous studies. Among these, understanding the relationship between humans and the spread of infection is of interest. In our study, we propose the SEIR model on a scale-free network (SFN), focusing on two different relationships: “intended and unintended contacts”. In our model, the former relation is represented as contact with adjacent nodes in a network, and the latter relation is represented as contact with any node in a network. We represent the number of people human meet per activity (η), and the rate of “intended contact” among the people who meet (β). As a result, we reveal that the activity of people in society with larger η or smaller β has a greater impact on the spread of infection. We also discuss which behaviors spread the infection based on our simulation and behavioral data from the questionnaire survey. Our insights are helpful in formulating behavioral guidelines or deregulation policies for pandemics.