This project aims to establish a hybrid modeling framework that integrates classical epidemiological models and machine learning models to advance wastewater-based epidemiology in tracking infectious diseases. The innovative framework will generate multiple metrics related to infection dynamics, propagating uncertainty for more accurate predictions, and providing actionable data to inform public health policy. Machine learning models will generate real-time case numbers from multistream data, including wastewater cconcentration of pathogens, which will be integrated into state-of-the-art epidemiological transmission models. The hybrid model framework will be evaluated using the large volumes of data generated during the COVID-19 pandemic, where the effectiveness of different interventions can be assessed. Moreover, an economic evaluation of wastewater-based epidemiology as an infectious disease monitoring tool will be conducted to assess its cost-effectiveness for low- to middle-income countries. The model will be demonstrated on selected sites in Thailand, India and Indonesia, where wastewater analysis has been deployed as a complementary surveillance of COVID.