Validation of Wastewater-Based Epidemiology Model Predictions and the Influence of Super-Shedders and Sewage Dynamics...

Wastewater-based epidemiology (WBE) monitors pathogens in sewage to estimate community disease trends and prevalence, often capturing cases missed by clinical reporting. WBE models use shedding data to facilitate WBE implementation and interpretation; however, their performance is uncertain because "true" case numbers are unknown. Hence, we compared model-predicted wastewater genome loads and detection rates of pepper mild mottle virus (PMMoV) and Carjivirus with values derived from wastewater data in literature. We found that predicted and observed wastewater DNA/RNA load distributions overlapped by 86.1% for PMMoV and 83.2% for Carjivirus, and that detection probabilities are within 5% of reported values in 14/15 and 13/14 studies, respectively, supporting the model as a robust tool for predicting wastewater detection likelihoods and guiding WBE applications. However, the median observed wastewater load exceeded the predicted distribution median in over half of all studies, suggesting that available shedding data underestimate wastewater concentrations due to higher shedding by a small population subset ("supershedders") or sewage network virus accumulation─using average shedding rates and WBE data without accounting for these factors would overestimate prevalence by 8.17-fold (PMMoV) and 3.75-fold (Carjivirus). This comparative analysis can be applied to other targets to improve WBE prevalence estimates and public health utility.