Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends

Wastewater-based epidemiology (WBE) is a useful complement
to clinical
testing for managing COVID-19. While community-scale wastewater and
clinical data frequently correlate, less is known about subcommunity
relationships between the two data types. Moreover, nondetects in
qPCR wastewater data are typically handled through methods known to
bias results, overlooking perhaps better alternatives. We address
these knowledge gaps using data collected from September 2020–June
2021 in Davis, California (USA). We hypothesize that coupling the
expectation maximization (EM) algorithm with the Markov Chain Monte
Carlo (MCMC) method could improve estimation of “missing”
values in wastewater qPCR data. We test this hypothesis by applying
EM-MCMC to city wastewater treatment plant data and comparing output
to more conventional nondetect handling methods. Dissimilarities in
results (i) underscore the importance of specifying nondetect handling
method in reporting and (ii) suggest that using EM-MCMC may yield
better agreement between community-scale clinical and wastewater data.
We also present a novel framework for spatially aligning clinical
data with wastewater data collected upstream of a treatment plant
(i.e., distributed across a sewershed). Applying the framework to
data from Davis reveals reasonable agreement between wastewater and
clinical data at highly granular spatial scalesfurther underscoring
the public-health value of WBE.