Making waves: Establishing a modeling framework to evaluate novel targets for wastewater-based surveillance

Area of Science:

• Environmental microbiology
• Public health surveillance
• Infectious disease epidemiology

Background:

• Wastewater-based surveillance (WBS) effectively monitors infectious diseases in communities.
• The suitability of WBS for novel or emerging pathogens remains uncertain.
• Current methods for assessing WBS feasibility are resource-intensive and lack interpretative context.

Purpose of the Study:

• To present a framework for analyzing the feasibility of WBS for various diseases.
• To improve the interpretation of WBS detections and non-detections.
• To guide the selection and expansion of WBS for new disease targets.

Main Methods:

• Developed a theoretical framework for WBS feasibility analysis.
• Identified and discussed five key factors influencing WBS detection probability: genome shedding rate, infection rate, wastewater usage, limit of detection, and PCR replicates.
• Analyzed the relationship between these factors and the likelihood of WBS target detection.

Main Results:

• The proposed framework theoretically identifies diseases appropriate for WBS.
• Understanding the five key factors enhances the interpretation of WBS data, especially for novel targets.
• The framework clarifies uncertainties in wastewater monitoring, supporting WBS expansion.

Conclusions:

• A systematic WBS feasibility framework can accelerate the adoption of WBS for novel pathogens.
• Quantifying factors influencing detection probability improves the reliability and utility of WBS.
• This approach supports investment and research in expanding WBS applications for public health.