# A Systematic Review of Methodological Approaches to SARS-CoV-2 Wastewater Surveillance

**Authors:** György Deák, Laura Lupu, Raluca Prangate

PMC · DOI: 10.3390/v18020205 · Viruses · 2026-02-04

## TL;DR

This paper reviews methods for tracking SARS-CoV-2 in wastewater to better understand virus spread and improve surveillance techniques.

## Contribution

The paper systematically reviews methodological approaches to SARS-CoV-2 wastewater surveillance and highlights effective techniques.

## Key findings

- Most wastewater-based epidemiology studies correlate SARS-CoV-2 RNA levels with epidemiological data.
- Variations in sampling and testing protocols hinder standardization in wastewater surveillance.
- Effective methods for overcoming inhibitors in wastewater during RNA extraction are emphasized.

## Abstract

Following the COVID-19 pandemic, researchers have increasingly focused on monitoring the spread of the virus and improving methods to detect changes in the SARS-CoV-2 genome. Although clinical surveillance provides direct and reliable results, it has limited applicability. Wastewater-based epidemiology (WBE) has therefore emerged as a valuable, non-invasive complementary tool for disease surveillance. It provides a comprehensive picture of virus circulation in a population, including asymptomatic individuals and those who do not seek healthcare. In addition, it facilitates early detection of outbreaks and the collection of epidemiologic data at the community level. However, WBE also presents technical challenges, including variations in sampling and testing protocols, the presence of inhibitors that affect viral RNA extraction, and the need for standardised procedures between studies. These challenges should be addressed for possible future infectious disease outbreaks. One of the challenges facing researchers was to develop efficient methods that could overcome the extraction and detection problems related to inhibitors present in wastewater. To this aim, this systematic review highlights the potential use of WBE, the variety of techniques, and the most effective methods for the detection and quantification of SARS-CoV-2 in wastewater samples. A reproducible electronic search of the literature was conducted in the Web of Science (WoS) and PubMed databases for articles published between 2020 and 2024. Our search revealed that the majority of observed WBE applications emphasised a correlation between SARS-CoV-2 RNA concentration trends in wastewater and epidemiological data. Another relevant issue that the articles often discussed and compared was the techniques used in different steps of sample processing, such as sample collection, concentration and detection, hence the lack of standardised procedures. This paper provides a framework regarding previous research on WBE to gain a better understanding that will lead to functional solutions.

## Linked entities

- **Diseases:** SARS-CoV-2 (MONDO:0100096), COVID-19 (MONDO:0100096)

## Full-text entities

- **Genes:** ORF10 (ORF10 protein) [NCBI Gene 43740576], ORF7b (ORF7b) [NCBI Gene 43740574], S (surface glycoprotein) [NCBI Gene 43740568] {aka spike glycoprotein}, JTB (jumping translocation breakpoint) [NCBI Gene 10899] {aka HJTB, HSPC222, PAR, hJT}, ERVK-6 (endogenous retrovirus group K member 6, envelope) [NCBI Gene 64006] {aka ERVK6, HERV-K(C7), HERV-K108, K-Rev, c-orf, cORF}, M (membrane glycoprotein) [NCBI Gene 43740571], ORF3a (ORF3a protein) [NCBI Gene 43740569], ORF7a (ORF7a protein) [NCBI Gene 43740573], ORF6 (ORF6 protein) [NCBI Gene 43740572], ORF1ab (ORF1a polyprotein;ORF1ab polyprotein) [NCBI Gene 43740578], E (envelope protein) [NCBI Gene 43740570], N (nucleocapsid phosphoprotein) [NCBI Gene 43740575]
- **Diseases:** pneumonia (MESH:D011014), injury to (MESH:D014947), Influenza (MESH:D007251), infectious disease (MESH:D003141), infection (MESH:D007239), WBE (MESH:D019292), COVID-19 (MESH:D000086382), atypical pneumonia (MESH:D011019)
- **Chemicals:** water (MESH:D014867), PEG (MESH:D011092), AlCl3 (MESH:D000077410), aluminium (MESH:D000535), Phi6 (-), 5-HIAA (MESH:D006897), PAC (MESH:C016213)
- **Species:** Cystovirus phi6 (no rank) [taxon 10879], Gammacoronavirus (genus) [taxon 694013], MHV [taxon 2845560], Severe acute respiratory syndrome coronavirus 2 (no rank) [taxon 2697049], Murine hepatitis virus (no rank) [taxon 11138], Meleagris gallopavo (common turkey, species) [taxon 9103], Porcine epidemic diarrhea virus (no rank) [taxon 28295], Ebola virus (no rank) [taxon 1570291], Alpharicinrhavirus blanchseco (species) [taxon 2843852], Human coronavirus OC43 (no rank) [taxon 31631], Sarbecovirus (subgenus) [taxon 2509511], Betacoronavirus (genus) [taxon 694002], Coronaviridae (family) [taxon 11118], Bovine coronavirus (no rank) [taxon 11128], Orthomyxoviridae (family) [taxon 11308], Norovirus (genus) [taxon 142786], Bovine orthopneumovirus (no rank) [taxon 11246], uncultured crAssphage (no rank) [taxon 1211417], Human immunodeficiency virus (species) [taxon 12721], Mengo virus (no rank) [taxon 12107], Bacteria Latreille et al. 1825 (Bacteria stick insect, genus) [taxon 629395], Bacteroides (genus) [taxon 816], Homo sapiens (human, species) [taxon 9606], Enterovirus C (no rank) [taxon 138950]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12945004/full.md

## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12945004/full.md

## References

319 references — full list in the complete paper: https://tomesphere.com/paper/PMC12945004/full.md

---
Source: https://tomesphere.com/paper/PMC12945004