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With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare’s Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts.