Wastewater-based surveillance (WBS) programmes increasingly monitor pathogens across large geographic areas, but aggregating asynchronous, heterogeneous site-level data into broader-scale indicators remains a methodological challenge. We developed a Bayesian hierarchical framework to estimate continuous latent wastewater concentration at broader geographic scales from asynchronous site-level observations. The model accounts for differences in measurement precision, catchment population size, and changes in site participation, while preserving temporal resolution and supporting real-time analysis. The framework was validated using simulated data and applied to wastewater concentration measurements of respiratory viruses collected between January 2024 and December 2025 from 25 sites in six provinces participating in Canada's National Wastewater Monitoring of Pathogens programme. Application of the model to observational data generated continuous provincial and national concentration estimates. At the national level, latent wastewater concentrations showed moderate to strong agreement with corresponding clinical test positivity data (R2 = 0.65--0.93). Sensitivity analyses indicated that estimates were robust to shorter fitting windows and to the addition or removal of surveillance sites. This framework provides a statistically coherent and operationally practical approach for aggregating asynchronous wastewater observations into population-level surveillance indicators, with potential applications across pathogens and public health jurisdictions.