Abstract
Estimating total infection levels, including unreported and asymptomatic infections, is important for understanding community disease transmission. Wastewater can provide a pooled community sample to estimate total infections that is independent of case reporting biases toward individuals with moderate to severe symptoms and by test-seeking behavior and access. We derive three mechanistic models for estimating community infection levels from wastewater measurements based on a description of the processes that generate SARS-CoV-2 RNA signals in wastewater and accounting for the fecal strength of wastewater through endogenous microbial markers, daily flow, and per-capita wastewater generation estimates. The models are illustrated through two case studies of wastewater data collected during 2020-2021 in Virginia Beach, VA, and Santa Clara County, CA. Median simulated infection levels generally were higher than reported cases, but at times, were lower, suggesting a discrepancy between the reported cases and wastewater data, or inaccurate modeling results. Daily simulated infection estimates showed large ranges, in part due to dependence on highly variable clinical viral fecal shedding data. Overall, the wastewater-based mechanistic models are useful for normalization of wastewater measurements and for understanding wastewater-based surveillance data for public health decision-making but are currently limited by lack of robust SARS-CoV-2 fecal shedding data.