The model considers a weighted network G = ( V , E t , W t ) with stable agents V , time - varying links E t , and link weights W t . We define the agents V and assigned them to specific CBGs based on the population size of each CBG documented in the ACS demographic data . Then using the dwelling locations of the devices provided in the Veraset data , we considered two devices to be in contact if they presented in the same POI at the same time . Here , the dwelling locations of the devices are defined according to the time the devices spent in a location . We use 5 mins as the threshold amount of time to filter out the locations where the devices might be waiting for traffic lights . The length of the overlap of their time spent in the POI is denoted as the duration of their contact . As such , we create an empirical weighted contact network among the devices in the Veraset data . In addition , we estimated the home CBGs of the devices . The links between any pair of CBGs can be extracted . Based on the proportion between the population size of empirical data and the synthetic population , we have a scalar to quantify the difference between the number of devices and synthetic agents . The scalar is then applied to determine the number of links that should be synthesized for each pair of CBGs . The weights of these links follow the distribution of the weights in the empirical contact network . Next , we assign the links to the synthetic agents in these two CBGs , where the degree distribution for the agents is consistent with the degree distribution of the devices in the same CBGs in the empirical data . We generate and assign links by repeating the above steps for all pairs of CBGs . This approach allows for creating synthetic contact networks based on actual mobility data and to maintain key structural and attribute information of the agents and the networks .