Estimating Individual Transmission Rates and Basic Reproduction Numbers of Respiratory Diseases in Indoor Gatherings Using UWB-Based Location Data

Respiratory diseases, including COVID-19, influenza, MERS-CoV, pertussis, and the common cold, remain significant public health concerns, particularly in indoor environments such as schools and universities. A major challenge in infectious disease modeling is the estimation of transmission rates between individuals. Previous studies have largely focused on population-level or spatiotemporally averaged transmission measures, leaving individual contributions to disease spread insufficiently characterized. To address this gap, we deployed an Ultra-Wideband (UWB)-based Real-Time Location System (RTLS) to collect high-resolution positional data during four social gatherings on a University campus. These data were used to perform contact analysis and to estimate individual-level transmission rates and reproduction numbers for multiple respiratory diseases. By moving beyond aggregated estimates to person-specific transmission metrics, this framework captures heterogeneity in contact behavior and provides a more realistic representation of indoor transmission dynamics. Such individual-level characterization has important implications for improving infectious disease modeling and strengthening targeted public health surveillance in shared indoor spaces. To facilitate adoption and reproducibility, we further developed an open-source Python package that enables researchers and public health practitioners to estimate individual transmission metrics from indoor tracking data and apply these methods to the analysis of respiratory disease spread in real-world settings.