Back

Balancing Accuracy and Actionability: An Assessment of Minimal-Input Wastewater Models for COVID-19 Prediction

Lariscy, L. M.; Lott, M. E.; Handel, A.; Foley, A. M.; Melendez-Declet, C.; Metsker, L.; Lipp, E. K.

2025-07-05 epidemiology
10.1101/2025.07.03.25330828 medRxiv
Show abstract

Wastewater-based epidemiology (WBE) is a valuable tool for surveillance of infectious diseases like COVID-19, yet balancing prediction accuracy with practical deployment remains challenging, particularly in low- resource, low-prevalence, and early warning contexts. We analyzed a two-and-a-half-year dataset from Athens-Clarke County, Georgia (USA) to compare approaches for predicting COVID-19 cases from SARS-CoV-2 wastewater data. We evaluated the effects of extraction replicates and use of quantitative versus detection frequency data on model performance using random forest and linear models. Results show that two extraction replicates generally suffice for reliable prediction, supporting efficient resource use. Combined viral load and detection frequency produced the strongest models, but detection frequency alone predicted new COVID-19 cases more accurately than viral load alone and was less sensitive to RT-qPCR instrumentation changes, making it a practical alternative when quantification is unreliable/infeasible. Linear models predicted new cases more accurately than random forest models, offering a resource-efficient option for monitoring wastewater trends. Following declines in clinical testing in spring 2022, wastewater- based models estimated substantially higher case counts than were reported, underscoring WBEs role for ongoing surveillance of COVID-19 and other infectious diseases. These findings provide practical guidance for optimizing WBE implementation, particularly where early warning and resource constraints are significant factors. HIGHLIGHTSO_LIAnalysis of SARS-CoV-2 in wastewater revealed that presence/absence rates in wastewater predicted new COVID-19 cases better than viral quantity alone C_LIO_LITwo extraction replicates was sufficient for acceptable prediction accuracy C_LIO_LILinear regression models more accurately predicted new cases than random forest C_LIO_LIAfter clinical testing efforts diminished, wastewater models predicted higher case counts than were reported C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/25330828v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@189e7b8org.highwire.dtl.DTLVardef@cc1102org.highwire.dtl.DTLVardef@1709ed1org.highwire.dtl.DTLVardef@cab23b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Journal of Water and Health · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.