Adaptive Sampling Design for Estimating Spatiotemporal Pathogen Prevalence in Cities

Jeffrey Bethel Co-Author
Oregon State University
 
Nicole Breuner Co-Author
Oregon State University
 
Benjamin Dalziel Co-Author
Oregon State University
 
Kathryn Higley Co-Author
Oregon State University
 
Allison Myers Co-Author
Oregon State University
 
Justin Preece Co-Author
Oregon State University
 
Tyler Radniecki Co-Author
Oregon State University
 
Katherine McLaughlin First Author
Oregon State University
 
Katherine McLaughlin Presenting Author
Oregon State University
 
Tuesday, Aug 6: 8:35 AM - 8:50 AM
3588 
Contributed Papers 
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.

Keywords

adaptive sampling

COVID-19

prevalence estimation

spatial sampling

wastewater-based epidemiology 

Main Sponsor

Survey Research Methods Section