Coupling wastewater-based epidemiology with data-driven machine learning for managing public health risks

Area of Science:

• Public Health
• Environmental Science
• Data Science

Background:

• Traditional health surveillance faces limitations in speed, coverage, and resources.
• Wastewater-based epidemiology (WBE) offers a cost-effective, rapid method for disease detection via sewage analysis.
• Big data analytics advancements enable predictive modeling and early risk detection in public health.

Purpose of the Study:

• To explore the application of machine learning (ML) in analyzing wastewater-based epidemiology data for infectious disease surveillance and forecasting.
• To highlight the advantages of ML-driven WBE models in processing multimodal data and predicting disease trends.
• To examine challenges and opportunities in integrating ML-WBE analytics into public health infrastructure.

Main Methods:

• Review and analysis of existing literature on machine learning applications in wastewater-based epidemiology.
• Exploration of ML model capabilities for processing complex WBE datasets.
• Discussion of simulation scenarios for policy impact evaluation using ML-WBE data.

Main Results:

• ML-driven WBE models can process multimodal data for enhanced infectious disease surveillance.
• These models enable accurate prediction of disease trends and evaluation of public health policy impacts.
• Integration of ML and WBE facilitates rapid data collection, analysis, and interpretation beyond traditional methods.

Conclusions:

• The synergy between WBE and ML significantly enhances infectious disease surveillance and forecasting capabilities.
• ML-WBE analytics can reduce cognitive biases and improve data-driven responses to public health threats.
• This integrated approach holds substantial potential for improving public health outcomes amidst evolving global health risks.