# A framework for evaluating implementation, impact, and cost-effectiveness of wastewater and environmental surveillance

## TL;DR

This paper introduces a framework to evaluate wastewater surveillance programs for infectious disease tracking, focusing on effectiveness, cost, and impact.

## Contribution

A modular, evidence-based logic model for evaluating wastewater and environmental surveillance programs is introduced.

## Key findings

- The framework is based on 151 WES evaluations from 2016 to 2025.
- It aims to reduce disease burden and strengthen public health resilience.
- The model supports diverse evaluation types and aligns with public health principles.

## Abstract

Wastewater and environmental surveillance (WES) enhances infectious disease outbreak awareness by detecting pathogens before symptoms or clinical testing. As communities seek to implement, optimize, or expand WES programs, evaluation is essential to ensure effectiveness, cost efficiency, and trustworthiness. However, program evaluation guidance, including a logic model, is lacking. This conceptual analysis addresses this gap by presenting a modular, evidence-based logic model grounded in 151 WES evaluations from 2016 to 2025 across diverse contexts and program types. The model aligns with Kellogg Foundation principles and supports multiple evaluation types. It outlines inputs, activities, outputs, and outcomes leading to three ultimate goals: reducing infectious disease burden, lowering risks from catastrophic biological events, and strengthening public health system resilience. We offer recommendations on how public health agencies can use this model to guide planning and evaluation of WES implementation or expansion, ensuring stronger preparedness and response to future public health threats.

Flowchart illustrating a framework for evaluating wastewater and environmental surveillance, with sections labeled Inputs, Activities/Outputs, Immediate Outcomes, Intermediate Outcomes, and Ultimate Outcomes, listing related elements such as funding, sample analysis, public health decisions, and disease burden reduction.