Abstract: Assessment of COVID-19 pandemic predictions indicates that differential equation-based epidemic spreading models are less than satisfactory in the contemporary world of intense human connectivity. Network-based simulations are more apt for studying the contagion dynamics due to their ability to model heterogeneity of human interactions. However, the quality of predictions in network-based models depends on how well the underlying wire-frame approximates the real social contact network of the population. In this paper, we propose a framework to create a modular wire-frame to mimic the social contact network of geography by lacing it with demographic information. The proposed inter-connected network sports small-world topology, accommodates density variations in the geography, and emulates human interactions in family, social, and work spaces. The resulting wire-frame is a generic and potent instrument for urban planners, demographers, economists, and social scientists to simulate different "what-if" scenarios and predict epidemic variables. The basic frame can be laden with any economic, social, urban data that can potentially shape human connectance. We present a preliminary study of the impact of variations in contact patterns due to density and demography on the epidemic variables.