In recent times, COVID-19 is the most severe epidemic disease and it needs to be controlled as soon as possible. Promising ideas and mathematical models have been proposed to predict the number of infected people in a particular timeline and project its development tendency. In this paper, to further increase the accuracy of prediction, we propose a new model named AMSD model at an agent scale by combining three models that are widely used in this field: the social network model, the mobility model, and the Susceptible-Exposed-Infected-Recovered (SEIR) model. Initially, the mobility model could identify people's mobility patterns on weekdays and weekends, and during the day and at night. Then, by combining this model with the social network model, we could classify people by their social connections in the network, with a more accurate prediction of infected people. The basic SEIR model is enhanced to find the spread/growth of viruses between and within people and has four stages from susceptible to recovered. AMSD model, as the combination of these three models is a more comprehensive approach to better present and predict the propagation of COVID-19, which involves many more important social factors.