Transmission Modeling with Smartphone-Based Sensing

Abstract: Infectious disease spread is difficult to measure and model accurately because of uncertainties regarding the dynamics of mixing behavior and balancing simulation-generated estimates with empirical data. Smartphone-based sensing data promises the availability of inferred proximity contacts and improved transmission models. This talk will present a Kalman filter-based approach to integrating transmission models with smartphone-based sensing data inferred proximity contacts. Furthermore, this talk demonstrates studies of temporal-spatial resolution affecting transmission model simulation results, addressing which level of sensing resolution is required to capture proximity contacts improving transmission models.

Biography: Weicheng Qian is a Ph.D. Candidate with the Department of Computer Science supervised by Prof. Kevin Stanley and Prof. Nathaniel Osgood. His research interests broadly include applications of simulation modeling, data science, and machine learning to solve public health-related problems. Weicheng's MS thesis involved a study of human mobility patterns with smartphone-based sensing.