Assessing wastewater detection limit for dengue virus via urinary and fecal shedding modeling

Dengue virus (DENV), the most clinically significant arbovirus, represents a growing global public health concern. The true incidence of DENV infection is likely underestimated, as more than half of infections are asymptomatic and therefore unreported. Wastewater-based surveillance (WBS) has emerged as an effective approach for monitoring enteric and respiratory viruses with substantial asymptomatic infections, yet critical knowledge gaps remain regarding the extent of arboviral shedding into wastewater, especially that from asymptomatic individuals. In this study, urinary and fecal DENV RNA shedding loads were quantified in both symptomatic and asymptomatic infections to evaluate their relative contributions to wastewater signals. Three RNA extraction methods were first compared to identify the most effective protocol for processing urine and fecal samples, after which a large-volume extraction approach was applied. Using this optimized method, DENV RNA was detected in 11% (8/71) of the fecal samples, providing the first evidence of DENV detection in human feces. Specifically, urine represented the predominant route of DENV RNA shedding, with symptomatic infections exhibiting higher urinary RNA detection rates (82% vs 57%, p = 0.019) and viral loads (mean 471 vs 102 copies/mL, p = 0.007) than asymptomatic infections. Monte Carlo simulations further indicated that a minimum infection rate of 4% is required to ensure DENV detection in wastewater, providing critical insights for selecting optimal sampling locations and wastewater processing volumes. These findings establish an evidence-based framework for assessing the feasibility of DENV detection through WBS and provide a scalable approach to optimize environmental surveillance design in resource-limited settings, supporting earlier outbreak detection and strengthening global public health responses to dengue and other arboviral diseases.