Wastewater-based epidemiology offers a promising and less biased alternative to current passive surveillance methods for respiratory viruses, demonstrating correlations with reported cases, positivity rates, and hospitalizations. However, modeling disease dynamics based on wastewater presents challenges due to the potential impact of various factors on the accuracy and reliability of the data. This project addresses knowledge and methodology gaps in using wastewater data to monitor viral respiratory diseases by developing and implementing a comprehensive modeling framework incorporating data filtering methods, spatial-temporal modeling, and optimization techniques.
