A review of AI/ML approaches in wastewater surveillance advancement

Comprehensive Summary

Wastewater based epidemiology (WBE) has become an important tool for early detection and monitoring infectious diseases, especially during the COVID-19 pandemic. This article looks at how artificial intelligence and machine learning models have been used for wastewater surveillance over the past five years. The authors compared supervised, unsupervised, deep learning, and time series models based on predictive accuracy, scalability, interpretability, computational demand, and real time feasibility. Random Forest achieved an R^2 of 0.80 and RMSE of 0.54 in COVID-19 trend forecasting which outperformed linear regression. Support Vector Machines improved pathogen classification accuracy by about 20% compared with traditional techniques. Artificial Neural Networks estimated pathogen prevalence with correlation values between 0.81 and 0.92 and Long Short Term Memory networks reached R^2 values of approximately 0.81 in testing and 0.94 in training for multi community forecasting. This article provides strong frameworks for the issue of the monitoring of infectious diseases.

Outcomes and Implications

AI driven wastewater surveillance can improve early detection of infectious disease outbreaks before widespread clinical cases come up. Higher predictive accuracy, such as the R^2 values reported for Random Forest and LSTM models allows for more reliable public health forecasting. Improved pathogen classification through models like Support Vector Machines can enhance the identification of specific infectious agents in community wastewater. Time series machine learning models demonstrating lower RMSE and MAE values show stronger real time monitoring compared with traditional ARIMAX approaches. Overall, using hybrid AI models and environmental metadata can strengthen public health decision making.