COVID-19 is the most acute global public health crisis of this century. Current trends in the global infected and death numbers suggest that human mobility leading to high social mixing are key players in infection spread, making it imperative to incorporate the spatiotemporal and mobility contexts to future prediction models. In this work, we present a generalized spatiotemporal model that quantifies the role of human social mixing propensity and mobility in pandemic spread through a composite latent factor. The proposed model calculates the exposed population count by utilizing a nonlinear least-squares optimization that exploits the intrinsic linearity in SEIR (Susceptible, Exposed, Infectious, or Recovered). We also present inverse coefficient of variation of the daily exposed curve as a measure for infection duration and spread. We carry out experiments on the mobility and COVID-19 infected and death curves of New York City to show that boroughs with high inter-zone mobility indeed exhibit synchronicity in peaks of the daily exposed curve as well as similar social mixing patterns. Furthermore, we demonstrate that several nations with high inverse coefficient of variations in daily exposed numbers are amongst the worst COVID-19 affected places. Our insights on the effects of lockdown on human mobility motivate future research in the identification of hotspots, design of intelligent mobility strategies and quarantine procedures to curb infection spread.