Comparing the Use of Mobile Device and Flow Data to Characterize Dynamic Populations in Wastewater Surveillance Monitoring Areas

Mobile device data has the potential to capture fluctuations in populations contributing to a wastewater catchment area, which can aid in interpreting pathogen concentrations. As an alternative to flow rates which are commonly used to normalize pathogen concentrations, we evaluated the use of mobile device data to characterize population dynamics at the daily and monthly scale across 66 sewersheds in Colorado from August 2020 to December 2024. We compared monthly device counts to monthly average flow rates where available and evaluated the extent to which rainfall and snowmelt explained periods of decoupling between device counts and flow rates. Using K-Means clustering, we identified two distinct patterns of population dynamics, one comprised of high-tourism regions. There were significant temporal fluctuations in daily and monthly device counts, with more pronounced patterns at the monthly level. Flow and mobile device counts were not consistently well-correlated. Using mixed effects regression, we found that rainfall and snowmelt, in addition to device counts, were significantly associated with flow rates. Mobile device data can characterize dynamic populations over multiple geographical areas and over short and long timescales in ways that are distinct from flow. It should be considered in efforts to improve normalization algorithms for wastewater-based epidemiology.