Adaptive Sampling Design for Estimating Spatiotemporal Pathogen Prevalence in Cities

Oregon Convention Center 

Cities are fragile to pandemic threats but predictive awareness and emergent response can fundamentally change epidemic dynamics. We leverage passive data collection via wastewater-based epidemiology in disjoint microsewersheds that span a city to develop an ongoing and adaptive monitoring protocol for infectious diseases such as COVID-19 and influenza. Adaptively sampling in cities has the potential to efficiently detect hotspots and identify the emergence pathogens even when prevalence in the general population is very low. Each week, we utilize the past time series of wastewater pathogen concentration measurements, Census data at the block level, and information provided by local public health officials to select the next sampling locations to minimize the opportunity cost and efficiently reduce uncertainty in parameter values. In this talk, we describe the adaptive sampling design, derive appropriate estimators and standard errors, and discuss their statistical properties including robustness to small sample size, missing data, and measurement error. We present preliminary results from data collected from February-July, 2024 in three cities in Oregon.

adaptive sampling

COVID-19

prevalence estimation

spatial sampling

wastewater-based epidemiology 

Survey Research Methods Section