[Paper Review] Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey

Chen Chen, Wang, Yunfan+11 more|PubMed|Mar 22, 2024

SARS-CoV-2 detection and testing4 citations

TL;DR

This survey synthesizes current research on wastewater-based epidemiology (WBE) for SARS-CoV-2 surveillance, covering sampling, testing, and data analytics. It identifies key challenges such as viral shedding variability and signal degradation, and proposes a framework to integrate WBE with clinical data for improved infectious disease monitoring.

ABSTRACT

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Motivation & Objective

• To address the limitations of traditional clinical testing for COVID-19 surveillance, including low coverage and data latency.
• To evaluate the feasibility and reliability of wastewater-based epidemiology (WBE) as a complementary tool for community-level disease tracking.
• To systematize knowledge on wastewater sampling, viral quantification, and data analysis methods for SARS-CoV-2.
• To identify open challenges in WBE, such as signal degradation, population representativeness, and data integration with clinical surveillance.
• To provide a foundation for extending WBE to other infectious diseases beyond COVID-19.

Proposed method

• Systematic review and synthesis of peer-reviewed literature on WBE for SARS-CoV-2 from 2020 to 2023.
• Categorization of wastewater sampling techniques, including grab sampling, composite sampling, and automated sampling at WWTPs or manholes.
• Analysis of viral quantification methods, including RT-qPCR and digital droplet PCR for SARS-CoV-2 RNA detection in influent samples.
• Evaluation of data analytics techniques, such as normalization by flow rate, population-adjusted viral load, and time-series modeling for trend detection.
• Compilation and comparison of global WBE surveillance programs, including frequency, geographic scope, and data availability.
• Integration of WBE data with clinical case reporting to assess alignment and complementarity in outbreak detection.

Figure 1: Overview of Wastewater-based Epidemiology Surveillance System.

Experimental results

Research questions

• RQ1 How do different wastewater sampling strategies affect the reliability and representativeness of SARS-CoV-2 detection in community populations?
• RQ2 To what extent can wastewater viral load serve as a proxy for community-level SARS-CoV-2 prevalence, and what factors distort this relationship?
• RQ3 What are the key technical and methodological challenges in standardizing WBE across diverse geographic and infrastructural contexts?
• RQ4 How can WBE data be effectively combined with clinical surveillance data to improve early warning and outbreak prediction?
• RQ5 What are the most effective data analytics and modeling approaches for transforming raw wastewater data into actionable public health insights?

Key findings

• Wastewater-based surveillance provides a timely, population-level snapshot of SARS-CoV-2 prevalence, often detecting trends 1–2 weeks before clinical case reports.
• Viral load in wastewater is significantly affected by dilution in sewers, in-sewer degradation, and variable viral shedding, leading to underestimation of true community prevalence.
• Population normalization and flow rate adjustment are essential for comparing viral loads across different sewersheds and time points.
• Global WBE programs vary widely in frequency and data transparency, with 14 countries reporting data weekly or more frequently, while others lack public access.
• Integration of WBE with clinical data improves outbreak detection sensitivity, especially in low-testing or asymptomatic transmission settings.
• The methodological framework developed in this survey is generalizable to other infectious diseases, as demonstrated by existing WBE applications for polio, flu, and illicit drugs.

Figure 2: The overview of wastewater data analytics.