The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1–2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

  • Update on systematic review of environmental surveillance of SARS-CoV-2 as an early-warning system for public health.

  • Evidence on early detection of new waves of SARS-CoV-2 infection with an average lead time of 1–2 weeks.

  • ES could function as an EWS in given geographic areas where VOCs are still not reported.

Environmental surveillance (ES) of SARS-CoV-2 in wastewater has emerged as a supporting monitoring system for public health decision-making (World Health Organization 2022a). ES demonstrates the presence of SARS-CoV-2 in wastewater shed from both symptomatic and asymptomatic people offering the foundation for providing objective measures from a non-invasive and anonymous community-level sampling (Keshaviah et al. 2023). ES can support public health officials in informed decision-making by providing additional evidence on the circulation and trends of the virus and variants in wastewater and as a potential early-warning system (EWS) (World Health Organization 2022a). However, there is a need to address the main challenges related to international coordination on sampling design harmonization, analytical methods, and data interpretation to improve its usefulness as a public health tool with added value as EWS (Hyllestad et al. 2022).

A systematic review identifying and synthesizing evidence on the effectiveness of ES of SARS-CoV-2 as an EWS during the first year of the pandemic was published in July 2022 (Hyllestad et al. 2022). In this study, Hyllestad et al. considered 35 publications out of the 1,014 identified during the literature search. From the 35 publications, the authors found evidence for a potential of 1–2 weeks of ES in the early warning of waves of SARS-CoV-2 infection, while there was less evidence for an added value in the early detection of new variants of concerns (VOCs) due to the few publications identified. Furthermore, they identified the need for additional studies addressing methodological knowledge gaps, harmonized protocols for data comparison, guidelines for data interpretation, and information about the cost–benefit value of ES for public health decisions.

Since the publication of the above-mentioned systematic review, the number of research publications in this field has increased rapidly. In addition, a number of literature reviews have been conducted and published between June 2021 and August 2022, covering a wide range of subjects on ES of SARS-CoV-2. Li et al. (2022a) reviewed SARS-CoV-2 shedding sources and concentrations in wastewater. Some publications have reviewed the factors influencing wastewater sampling such as the capacity of the operators at the treatment plant or the alternative use of different sampling approaches (Bertels et al. 2022; Bivins et al. 2022; Hill et al. 2022). Several reviews have addressed and evaluated different analytical methods for the detection and quantification of SARS-CoV-2 (Basavaraju et al. 2021; Ahmed et al. 2022a; Mazumder et al. 2022; Zhang et al. 2022a). Furthermore, some authors reviewed the future potential of nanobiotechnology and biosensors for pathogen detection in wastewater, with advantages such as miniaturization of the detection assay, device portability, in-field automation, or multiplex detection as promising surveillance alternatives (Mackul'ak et al. 2021; Rahman et al. 2021; Jimenez-Rodriguez et al. 2022; Mahmoudi et al. 2022). However, the lack of methodological standardization continues to be one of the major barriers to evaluating and implementing ES data into disease surveillance programs (McClary-Gutierrez et al. 2021a; Kumblathan et al. 2022). The potential of near-to-source or decentralized wastewater sampling was reviewed by several authors, with a special focus on student campuses (Harris-Lovett et al. 2021; Korfmacher et al. 2021; Goncalves et al. 2022; Kapoor et al. 2022; Sweetapple et al. 2022). The feasibility of ES of SARS-CoV-2 in wastewater in low- and middle-income countries (LMICs) has also been reviewed (Shrestha et al. 2021; Dzinamarira et al. 2022; Gwenzi 2022; Medina et al. 2022). The vast majority of the reviews assessed the potential, future opportunities, challenges and needs for ES (Amereh et al. 2021; Bonanno Ferraro et al. 2021; Hill et al. 2021; Lundy et al. 2021; Mainardi & Bidoia 2021; Mousazadeh et al. 2021; Pulicharla et al. 2021; Sharara et al. 2021; Adhikari & Halden 2022; Alhama et al. 2022; Faraway et al. 2022; Hrudey & Conant 2022; Kasprzyk-Hordern et al. 2022; Korfmacher & Harris-Lovett 2022; Lowe & Bencko 2022; Soni et al. 2022; Wu et al. 2022a), with a special focus on its potential as EWS for emerging infectious diseases (Bibby et al. 2021; Oeschger et al. 2021; Olesen et al. 2021; Zhu et al. 2021; Kumar et al. 2022). In addition, Zhang et al. (2022b) discussed the cost-effectiveness of ES to prevent future nationwide outbreaks of COVID-19).

Considering the rapid increase and expansion of knowledge in this field, the aim of this work is to update the systematic review evaluating the effectiveness of ES of SARS-CoV-2 as an EWS for public health decisions during the second year of the pandemic.

The main aim includes the following objectives:

  • To assess the effectiveness of ES as an EWS for SARS-CoV-2 in terms of timeliness, sensitivity, and specificity.

  • To assess ES ability to detect the early introduction of new variants into wastewater.

  • To evaluate the public health impact and control measures related to ES at national and international levels.

Study protocol and search strategy

The present systematic review was conducted following the predefined protocol previously registered in PROSPERO International Prospective Register of Systematic Reviews with registration number CRD42021261383 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021261383). Since this review is an update of our previous work (Hyllestad et al. 2022), the terms in our search strategies remained the same with the exception of the study period including publications between 15 June 2021 and 31 July 2022.

The search was performed using six international online databases of scientific literature (MEDLINE, Embase, Web of Science Core Collection, Scopus, Cochrane, and Epistemonikos). The database searches were complemented with manual web searches to identify relevant ‘grey literature’. A grey literature search was made on the national authorities' health and infection control websites and at related larger international organizations in the following languages: Norwegian, English, Swedish, Danish, German, Spanish, and Italian. Search strategy, information sources, data management and extraction are described in Supplementary Information. The reporting of this systematic review was guided by the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.

Screening and eligibility criteria

Three independent reviewers (S.H., E.A., and J.B.) selected the eligible publications based on title and abstract using Rayyan software (Ouzzani et al. 2016). Publications were considered eligible for inclusion if (i) they focused on the analysis of SARS-CoV-2 in wastewater and its comparison with public health data or (ii) focused on the detection of SARS-CoV-2 variants in wastewater, as well as those without clinical data comparison. The exclusion criteria for the publications were the following: (i) out of the scope (i.e., only analytical method development or virus detection, but no comparison with clinical data), (ii) review publications, conference abstracts or letters to the editor, (iii) pre-prints, or (iv) outside the defined time window.

The full-text screening of the selected publications used for the assessment of the effectiveness of ES as EWS was performed by J.B. J.P. performed the full-text screening of the selected publications used for the assessment of the early introduction of new variants. E.A. and M.M. independently reviewed and assessed the eligibility of the publications selected for the effectiveness of EWS and early introduction of variants, respectively. J.B. and J.A. conducted the screening of the grey literature for public health impact.

Data synthesis

The data were summarized using a template form including the following information from the publications to fulfill the aim and objectives of the review: country and location, number of sampling points, study population, study period, type of sampling, storage conditions, detection method, targeted genes, type of normalization, clinical data used for comparison and estimated timeliness as EWS. Publications reporting public health response or control measures were highlighted to be used in addition to the grey literature for the evaluation of the public health impact and control measures related to ES. Publications identified in the grey literature were classified into six different categories: evaluation of ES compared to clinical data, ES as EWS, cost-effectiveness, public health usefulness of ES for decision-making, best practice guidelines for ES methods, and ethical aspects of ES (Supplementary information, Table S1).

Furthermore, the type of comparison between wastewater and clinical data was included in the publications performed at the population level. The specific sampling location was included in the publications performed at hotspot or near-to-source areas. Finally, information about variant detection methods, next-generation sequencing (NGS) platforms, NGS primers, variant reporting and variant classification was included in the publications on the early introduction of new variants. In addition, we screened the affiliation of the co-authors from the publications included in the review process of the effectiveness of ES as an EWS as a proxy to assess the distribution of the professional sectors. We grouped this information into four categories for discussing the effectiveness of ES as an EWS: water, wastewater, and sanitation agencies (W), environmental research institutions (E), public health research institutions (H), and public health authorities (P).

The extracted data providing information about the three main objectives of this study were not suitable for pooling due to heterogeneity. Therefore, a meta-analysis could not be performed. The information was synthesized in a tabular form with a narrative summary of the findings. A full description of all the descriptors and all data collected can be found in Supplementary Information.

Assessment of risk of bias in the individual studies and cumulative evidence

The ROBINS-I assessment tool, version 1 August 2016 (Sterne et al. 2016) was applied to address the risks of bias in individual (i.e., non-randomized) studies. The body of evidence comprised of the cumulative results was assessed by the Project on a Framework for Rating Evidence in Public Health (PRECEPT), which was developed by the European Centre for Disease Prevention and Control (ECDC) in 2012 (Harder et al. 2017). The criteria’ definitions and interpretation of the seven confounding domains applied in this systematic review and the risk of bias levels for each article can be found in Supplementary Information.

Ethical considerations

The current study did not require ethical approval because we did not collect any sensitive personal data or health information. The analysis included only published articles on the research topic.

Descriptive summary of study characteristics

Of the 1,588 publications obtained from the literature search, 331 were assessed according to the study protocol through a full-text screening using Rayyan and 151 fulfilled the eligibility criteria. Of these 151 publications, 86 were included in the review to assess the effectiveness of ES as an EWS, 59 on early detection of SARS-CoV-2 variants and six covering both study objectives. Seven of the publications used for the assessment of the effectiveness were also included for the evaluation of the public health action and control measures, together with the 30 publications from the grey literature. A list of excluded publications with reasons for non-inclusion and a summary of the extracted data is available in the flow diagram in Table 1 and Supplementary Information.

Table 1

Flow diagram of the literature-screening process following PRISMA guidelines

 
 

North America accounted for 44% (n = 67) of the 151 publications followed by Europe with 33% (n = 50). Asia, South America, Oceania, and Africa contributed with 12, 5, 3 and 2% (n = 18, 8, 5, and 3), respectively. In terms of publications per country, the highest number of publications originated from the United States (n = 49), followed by Canada (n = 16) and Spain (n = 8) (Figure 1).
Figure 1

Country distribution of publications included in the systematic review: (a) number of studies per country included for the assessment of the effectiveness of ES as an EWS for SARS-CoV-2 and (b) number of studies per country included reporting SARS-CoV-2 variant detection in wastewater. A multi-country European study was not represented in (b).

Figure 1

Country distribution of publications included in the systematic review: (a) number of studies per country included for the assessment of the effectiveness of ES as an EWS for SARS-CoV-2 and (b) number of studies per country included reporting SARS-CoV-2 variant detection in wastewater. A multi-country European study was not represented in (b).

Close modal

Effectiveness of ES as an EWS

All included publications on ES as an EWS (n = 92) compared wastewater data on SARS-CoV-2 to COVID-19 confirmed case data. The publications were from different geographical areas, with the predominance in North America. The United States (n = 35) together with Canada (n = 12) and Mexico (n = 2) contributed with 53% of the total number of articles included. Europe contributed with 22 publications, South America, and Asia with nine each, Oceania with three, and Africa with two publications.

Regarding affiliation, 88% of the publications had contributions from environmental research institutions, 67% from public health research institutions, 33% from public health authorities and 20% from experts from water, wastewater, and sanitation agencies. Out of the 92 publications, 10% have authors representing four different domains, 17% three domains, 52% two domains and 21% authors from only one domain.

The wastewater surveillance study period included in the publications was carried out from January 2020 until January 2022. The approximate study period ranged from one to 20 months with a median of six months. Sampling frequency varied between publications, being daily, weekly, or biweekly being the most used. Most of the studies reported a transport temperature from the wastewater treatment plants (WWTPs) to the laboratory of 4 °C (n = 80, 87%). Six studies transported the samples at −20 °C, two at −80 °C, two at atmosphere temperature and three studies did not report the temperature. The estimated population covered by the wastewater systems, including one or more WWTPs, varied between ∼600 and ∼42,500,000 inhabitants (median of ∼1,500,000 inhabitants). Population estimates were not reported in nine of the included studies.

Most of the studies analyzed the wastewater samples through reverse transcription-quantitative polymerase chain reaction (RT-qPCR) (n = 83, 90%). Some studies used different techniques such as reverse transcription droplet digital PCR (RT-ddPCR) (n = 16), nested reverse transcription PCR (nRT-PCR) (n = 1) (Cariti et al. 2022) or liquid-chromatography mass-spectrometry (LC-MS/MS) (n = 1) (Lara-Jacobo et al. 2022). The N1, N2 and E genes were predominant as molecular targets (n = 72, 51 and 21, respectively) followed by the ORF1ab (n = 8), RdRp (n = 7), S (n = 6) and N3 (n = 3) genes. The approaches used for data normalization were wastewater flow data (n = 31), human fecal markers such as Pepper Mild Mottle Virus (PMMoV) (n = 28) or crAssphage (n = 8), chemical oxygen demand (COD) (n = 3) and human-specific Bacteroides HF183 (n = 2). Furthermore, 26 studies (28%) did not report any data normalization to correct for dilution events and population dynamics.

The number of wastewater sampling points monitored ranged from one to 492 (median of six sampling points). The sampling mode most frequently used was influent composite sampling for 24 hours (n = 74, 80%), except for two studies performing 3-h and two studies performing 4-h composite sampling. Composite sampling was either flow-, time- or volume-proportional. Grab sampling was applied in 32 studies, some in combination with composite sampling. Two studies performed passive sampling approaches using Moore swab samplers (Wang et al. 2022a) and NanoCeram column filters (Zhao et al. 2022). Sludge samples were analyzed in two studies (Anneser et al. 2022; Yanac et al. 2022). One study did not report the type of sampling used (Giraud-Billoud et al. 2021).

The assessment of the effectiveness of EWS was divided into two categories based on two different sampling strategies found in the literature: population level or targeted settings.

A total of 65 publications were performed at the population level by collecting samples from the main WWTP connected to the city or metropolitan area (Table 2). These studies evaluated the results by comparing the wastewater versus clinical data using different approaches. In 13 publications, wastewater data were plotted or compared with clinical data without any statistical approach. A total of 35 publications included in this section performed correlation analysis between clinical and wastewater-based SARS-CoV-2 data. Spearman's rank correlation and Pearson's correlation coefficient analysis were the most used. Analytical modeling, regression or machine learning models were used in 17 of the publications to predict future registered cases, estimate infection prevalence, the effective reproduction number or virus shedding.

Table 2

Synthesis of publications included to evaluate the effectiveness of ES as an EWS for COVID-19 at population level (n = 65)

Ref.CountryWWTPs or SPsPopulationStudy periodDetection methodNormalizationType comparisonCo-authors affiliation
EW window
(Location)(Target genes)(Clinical data)WEHP(days)
Cariti et al. (2022)  CH (Ticino) 9 WWTPs 330,000 2020/02
2020/03 
RT-qPCR, nRT-PCR (N1, N2, S) NR Comparison (Confirmed daily cases)  ✓  ✓ 
Daleiden et al. (2022)  AT (Tyrol) 43 WWTPs 740,000 2020/09
2021/07 
RT-qPCR (N1) Flow Rate, PMMoV Comparison (Confirmed daily cases)   ✓ ✓ 3–7 
Huang et al. (2021)  CA (Halifax) 1 WWTP 120,000 2020/10
2021/02 
RT-qPCR (N1, N2, N3, E) NR Comparison (Confirmed daily cases)  ✓   
Kuhn et al. (2022)  US (Oklahoma City) 13 SPs 230,000 2020/11
2021/03 
RT-qPCR (N1) NR Modeling (Confirmed daily cases)  ✓ ✓ ✓ 4–10 
Kumar et al. (2021)  IN (Ahmedabad) 1 WWTP, 8 SPs 6,350,000 2020/09
2020/11 
RT-qPCR (N, ORF1ab, S) no Correlation (Self-reporting)  ✓   7–14 
Lastra et al. (2022)  ES (Madrid) 289 SPs 6,000,000 2020/08
2021/05 
RT-qPCR (NR) NR Comparison (Confirmed daily cases, hospitalizations) ✓  ✓ ✓ 11 
Pardo-Figueroa et al. (2022)  PE (3 cities in PE) 5 WWTPs, 9 SPs 6,700,000 2021/02
2021/10 
RT-qPCR (N1) Flow Rate, COD Comparison (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  no 
Parra-Guardado et al. (2022)  CA (4 cities in CA) 4 WWTPs 270,000 2021/04
2021/07 
RT-qPCR (N, E, ORF1a, RdRp) no Comparison (Confirmed daily cases)  ✓  ✓ 
Pileggi et al. (2022)  CA (Ontario) 4 WWTPs 3,000,000 2021/03
2021/08 
RT-qPCR (N1, N2) Flow Rate, PMMoV Comparison (Confirmed daily cases)  ✓  ✓ no 
Pillay et al. (2022)  ZA (eThekwine) 4 WWTPs 3,200,000 2021/02
2021/12 
RT-ddPCR (N2) NR Correlation (Confirmed daily cases)  ✓   14–21 
Rodriguez Rasero et al. (2022)  ES (Sevilla) 8 SPs 160,000 2020/07
2021/02 
RT-qPCR (N1, N2) Flow Rate Modeling Confirmed daily cases) ✓   ✓ 
Rubio-Acero et al. (2021)  DE (Munich) 6 SPs 500,000 2020/04
2021/03 
RT-qPCR (N1) NR Correlation (Confirmed daily cases) ✓ ✓ ✓ ✓ 21 
Song et al. (2021)  US (California) 9 WWTPs 4,800,000 2020/04
2020/06 
RT-qPCR, RT-ddPCR (N1, N2) NR Comparison (Confirmed daily cases) ✓ ✓   
Sosa-Hernandez et al. (2022)  MX (Monterrey) 4WWTPs 4,600,000 2020/04
2021/02 
RT-qPCR (E) Flow Rate Comparison (Confirmed daily cases)  ✓   14 
Toledo et al. (2022)  US (New England) 9 WWTPs 200,000 2020/09
2021/02 
RT-qPCR, RT-ddPCR (N1, N2) PMMoV Comparison (Confirmed daily cases)   ✓  
Yanac et al. (2022)  CA (Winnipeg) 3 WWTPs 660,000 2020/07
2020/12 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  NR 
Arora et al. (2022)  IN (Jaipur) 9 WWTPs 1,200,000 2021/02
2021/06 
RT-qPCR (E, N, RdRp, ORF1ab) Yes -NR Correlation (Confirmed daily cases, mortality)  ✓ ✓  14–20 
Barua et al. (2022)  US (North Carolina) 4 WWTPs 370,000 2020/06
2020/12 
RT-qPCR, RT-ddPCR (N1, N2) No Correlation (Confirmed daily cases)  ✓ ✓  5–12 
de Freitas Bueno et al. (2022)  BR (11 cities in BR) 16 WWTPs, 13 SPs 5,600,000 2021/01
2022/01 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases)  ✓ ✓  NR 
Duvallet et al. (2022)  US (19 States in US) 55 SPs 12,500,000 2020/04
2021/05 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  NR 
Feng et al. (2021)  US (Wisconsin) 12 WWTPs 1,650,000 2020/08
2021/01 
RT-ddPCR (N1, N2) PMMoV, HF183 Correlation (Confirmed daily cases)  ✓  ✓ 
Giraud-Billoud et al. (2021)  AR (Mendoza) 2 WWTPs 1,000,000 2020/07
2020/11 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases, mortality) ✓ ✓ ✓  3–6 
Hoar et al. (2022)  US (New York) 14 WWTPs 8,600,000 2020/08
2021/04 
RT-qPCR, RT-ddPCR (N1) Flow Rate Correlation (Confirmed daily cases, hospitalizations)  ✓ ✓  no 
Isaksson et al. (2022)  SE (Luleå) 2 WWTPs 70,000 2021/01
2021/03 
RT-qPCR (N1) PMMoV, Flow Rate Correlation (Confirmed daily cases)  ✓   0–8 
Lara-Jacobo et al. (2022)  CA (Ontario) 2 WWTPs 300,000 2020/10
2021/04 
LC-MS/MS, RT-qPCR (N1) Flow Rate Correlation (Confirmed daily cases)  ✓   5–6 
Lazuka et al. (2021)  FR (10 cities in FR) 10 WWTPs 3,250,000 2020/07

2020/12 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases, hospitalizations, mortality)  ✓   NR 
Li et al. (2022bUS (Nevada) 3 WWTPs 390,000 2020/07
2021/09 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases) ✓ ✓   
Monteiro et al. (2022)  PT (2 areas PT) 5 WWTPs, 3 SPs 2,000,000 2020/04
2020/12 
RT-qPCR (N, E, RdRp) Flow Rate Correlation (hospitalizations) ✓ ✓   NR 
Nagarkar et al. (2022)  US (Cincinnati) 2 WWTPs 520,000 2020/05
2020/10 
RT-ddPCR (N1, N2) crAssphage, PMMoV, HF183 Correlation (Confirmed daily cases)  ✓  ✓ 7–14 
Nelson et al. (2022)  US (Austin) 2 WWTPs 1,800,000 2020/05
2022/01 
RT-qPCR (N2) Flow Rate Correlation (Confirmed daily cases)  ✓   1–7 
Padilla-Reyes et al. (2022)  MX (Monterrey) 4 WWTPs, 4 SPs 3,800,000 2020/06
2020/12 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases, mortality)  ✓   2–7 
Robotto et al. (2021)  IT (Piedmont) 4 WWTPs (out of 13) 1,700,000 2020/08
2021/03 
RT-qPCR (N1, N2, E) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  Few days 
Rothman et al. (2021)  US (South California) 7 WWTPs 16,000,000 2020/08
2021/01 
RT-ddPCR (N1) no Correlation (Confirmed daily cases)  ✓ ✓  NR 
Sangsanont et al. (2022)  TH (Bangkok) 19 WWTPs 2,750,000 2021/01
2021/04 
RT-qPCR (N1, N2) CrAssphage Correlation (Confirmed daily cases)  ✓ ✓  22–24 
Stephens et al. (2022)  NL (2 cities in NL) 2 WWTPs 900,000 2020/03
2021/05 
RT-qPCR (N1, N2, N3, E) CrAssphage Correlation (Confirmed daily cases, hospitalizations)  ✓   3–9 
Street et al. (2021)  ZA (Cape Town) 23 WWTPs 4,000,000 2020/07
2020/08 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Tandukar et al. (2022)  NP (Kathmandu Valley) 2 WWTPs, NR 2020/07
2021/02 
RT-qPCR (N1, N2) CrAssphage Correlation (Confirmed daily cases)  ✓ ✓  NR 
Tanimoto et al. (2022)  JP (Kobe) 2 WWTPs 780,000 2021/02
2021/10 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases) ✓  ✓  NR 
Tiwari et al. (2022)  FI (28 locations in FI) 28 WWTPs 3,300,000 2020/08
2021/05 
RT-qPCR (N2) Flow Rate, crAssphage Correlation (Confirmed daily cases)   ✓  NR 
Tomasino et al. (2021)  PT (Porto) 2 WWTPs 370,000 2020/05
2021/03 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases) ✓ ✓ ✓  NR 
Wang et al. (2021)  US (Los Angeles) 5 WWTPs 8,900,000 2020/05
2021/03 
RT-qPCR (N1, N2, E) Flow Rate Correlation (Confirmed daily cases)  ✓   
Wu et al. (2022bUS (Massachusetts) 1 WWTP 2,300,000 2020/01
2020/05 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  4–10 
Wu et al. (2021)  US (40 States in US) 353 SPs 42,500,000 2020/02
2020/06 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases, mortality)  ✓ ✓  NR 
Xiao et al. (2022)  US (Massachusetts) 1 WWTP 2,300,000 2020/03
2021/05 
RT-qPCR (N1, N2) PMMoV Modeling (Confirmed daily cases) ✓ ✓ ✓  1–6 
Zhan et al. (2022)  US (Miami) 3 WWTP, 1 SP 2,500,000 2020/09
2021/11 
RT-qPCR (N1, ORF1ab) PMMoV Correlation (Confirmed daily cases, hospitalizations)  ✓ ✓  0–7 
Zhao et al. (2022)  US (Detroit) 3 WWTPs 2,800,000 2020/09
2021/08 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases) ✓ ✓  ✓ 14–35 
Zheng et al. (2022)  HK (Hong Kong) 3 WWTPs 2,100,000 2020/12
2021/06 
RT-qPCR (N1, E) PMMoV, Flow Rate Correlation (Confirmed daily cases) ✓ ✓ ✓  NR 
Anneser et al. (2022)  US (Massachusetts) 5 WWTPs 2,600,000 2020/08
2021/03 
RT-qPCR (N1, N2, N3) PMMoV Modeling (Confirmed daily cases)  ✓ ✓  NR 
Claro et al. (2021)  BR (Sao Paulo) 2 WWTPs, 3 SPs 1,400,000 2020/06
2021/04 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  14 
Cluzel et al. (2022)  FR (All FR) 168 WWTPs 12,800,000 2020/03
2021/05 
RT-qPCR, RT-ddPCR (E, RdRp) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  
Fitzgerald et al. (2021)  GB (Scotland) 28 WWTPs 2,600,000 2020/05
2021/01 
RT-qPCR (N1, E) Flow Rate Modeling (Confirmed daily cases, mortality) ✓ ✓ ✓  NR 
Galani et al. (2022)  GR (Attica) 1 WWTP 3,700,000 2020/09
2021/03 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases, hospitalizations)  ✓ ✓  5–9 
Greenwald et al. (2021)  US (San Francisco) 6 SPs 2,600,000 2020/04
2020/09 
RT-qPCR (N1) PMMoV, crAssphage Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ 0–21 
Huisman et al. (2022)  CH (Zurich, San Jose) 2 WTPs 1,500,000 2020/11
2021/03 
RT-qPCR, RT-ddPCR (N1, N2) Flow Rate/PMMoV Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓ ✓ 1–4 
Koureas et al. (2021)  GR (Larissa and Volos) 2 WWTPs 150,000 2020/10
2021/04 
RT-qPCR (ORF1ab) Ammonium, COD, BOD, TSS Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Krivonakova et al. (2021)  SK (Bratislava) 2 WWTPs 575,000 2020/09
2021/03 
RT-qPCR (ORF1ab, S, E, RdRp) no Modeling (Confirmed daily cases, mortality)  ✓ ✓  12–26 
Morvan et al. (2022)  GB (All England) 45 SPs 17,300,000 2020/07
2021/03 
RT-qPCR (N1, E) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓ ✓ 4–5 
Nourbakhsh et al. (2022)  CA (3 cities in CA) 6 WWTPs 3,500,000 2021/03
2021/06 
RT-qPCR (N1, N2) PMMoV Modeling (Confirmed daily cases, hospitalizations)  ✓ ✓ ✓ NR 
Vallejo et al. (2022)  ES (A Coruña) 1 WWTP, 1 SP 370,000 2020/04
2020/06 
RT-qPCR (N) Flow Rate Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Amereh et al. (2022)  IR (Tehran) 7 WWTPs 5,600,000 2020/09
2021/04 
RT-qPCR (N, ORF1ab) Flow Rate Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  1–2 
de Sousa et al. (2022)  BR (Goiás) 1 WWTP 700,000 2020/01
2020/08 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  NR 
Jiang et al. (2022)  US (Utah) 47WWTPs 2,500,000 2020/05
2021/07 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  2–4 
Camphor et al. (2022)  AU (New South Wales) 7 WWTPs 3,793,038 2020/03
2020/07 
RT-qPCR (N1, N2) no Modeling (Confirmed daily cases)    ✓ NR 
Li et al. (2023)  CA (Alberta) 12 WWTPS 3,500,000 2020/05
2021/06 
RT-qPCR (N1, N2) no Modeling (Confirmed daily cases) ✓  ✓  NR 
Wolfe et al. (2021)  US (California) 8 WWTPs 4,000,000 2020/11
2021/03 
RT-ddPCR (N, S, ORF1ab) PMMoV Correlation (Confirmed daily cases)  ✓ ✓ ✓ NR 
Ref.CountryWWTPs or SPsPopulationStudy periodDetection methodNormalizationType comparisonCo-authors affiliation
EW window
(Location)(Target genes)(Clinical data)WEHP(days)
Cariti et al. (2022)  CH (Ticino) 9 WWTPs 330,000 2020/02
2020/03 
RT-qPCR, nRT-PCR (N1, N2, S) NR Comparison (Confirmed daily cases)  ✓  ✓ 
Daleiden et al. (2022)  AT (Tyrol) 43 WWTPs 740,000 2020/09
2021/07 
RT-qPCR (N1) Flow Rate, PMMoV Comparison (Confirmed daily cases)   ✓ ✓ 3–7 
Huang et al. (2021)  CA (Halifax) 1 WWTP 120,000 2020/10
2021/02 
RT-qPCR (N1, N2, N3, E) NR Comparison (Confirmed daily cases)  ✓   
Kuhn et al. (2022)  US (Oklahoma City) 13 SPs 230,000 2020/11
2021/03 
RT-qPCR (N1) NR Modeling (Confirmed daily cases)  ✓ ✓ ✓ 4–10 
Kumar et al. (2021)  IN (Ahmedabad) 1 WWTP, 8 SPs 6,350,000 2020/09
2020/11 
RT-qPCR (N, ORF1ab, S) no Correlation (Self-reporting)  ✓   7–14 
Lastra et al. (2022)  ES (Madrid) 289 SPs 6,000,000 2020/08
2021/05 
RT-qPCR (NR) NR Comparison (Confirmed daily cases, hospitalizations) ✓  ✓ ✓ 11 
Pardo-Figueroa et al. (2022)  PE (3 cities in PE) 5 WWTPs, 9 SPs 6,700,000 2021/02
2021/10 
RT-qPCR (N1) Flow Rate, COD Comparison (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  no 
Parra-Guardado et al. (2022)  CA (4 cities in CA) 4 WWTPs 270,000 2021/04
2021/07 
RT-qPCR (N, E, ORF1a, RdRp) no Comparison (Confirmed daily cases)  ✓  ✓ 
Pileggi et al. (2022)  CA (Ontario) 4 WWTPs 3,000,000 2021/03
2021/08 
RT-qPCR (N1, N2) Flow Rate, PMMoV Comparison (Confirmed daily cases)  ✓  ✓ no 
Pillay et al. (2022)  ZA (eThekwine) 4 WWTPs 3,200,000 2021/02
2021/12 
RT-ddPCR (N2) NR Correlation (Confirmed daily cases)  ✓   14–21 
Rodriguez Rasero et al. (2022)  ES (Sevilla) 8 SPs 160,000 2020/07
2021/02 
RT-qPCR (N1, N2) Flow Rate Modeling Confirmed daily cases) ✓   ✓ 
Rubio-Acero et al. (2021)  DE (Munich) 6 SPs 500,000 2020/04
2021/03 
RT-qPCR (N1) NR Correlation (Confirmed daily cases) ✓ ✓ ✓ ✓ 21 
Song et al. (2021)  US (California) 9 WWTPs 4,800,000 2020/04
2020/06 
RT-qPCR, RT-ddPCR (N1, N2) NR Comparison (Confirmed daily cases) ✓ ✓   
Sosa-Hernandez et al. (2022)  MX (Monterrey) 4WWTPs 4,600,000 2020/04
2021/02 
RT-qPCR (E) Flow Rate Comparison (Confirmed daily cases)  ✓   14 
Toledo et al. (2022)  US (New England) 9 WWTPs 200,000 2020/09
2021/02 
RT-qPCR, RT-ddPCR (N1, N2) PMMoV Comparison (Confirmed daily cases)   ✓  
Yanac et al. (2022)  CA (Winnipeg) 3 WWTPs 660,000 2020/07
2020/12 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  NR 
Arora et al. (2022)  IN (Jaipur) 9 WWTPs 1,200,000 2021/02
2021/06 
RT-qPCR (E, N, RdRp, ORF1ab) Yes -NR Correlation (Confirmed daily cases, mortality)  ✓ ✓  14–20 
Barua et al. (2022)  US (North Carolina) 4 WWTPs 370,000 2020/06
2020/12 
RT-qPCR, RT-ddPCR (N1, N2) No Correlation (Confirmed daily cases)  ✓ ✓  5–12 
de Freitas Bueno et al. (2022)  BR (11 cities in BR) 16 WWTPs, 13 SPs 5,600,000 2021/01
2022/01 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases)  ✓ ✓  NR 
Duvallet et al. (2022)  US (19 States in US) 55 SPs 12,500,000 2020/04
2021/05 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  NR 
Feng et al. (2021)  US (Wisconsin) 12 WWTPs 1,650,000 2020/08
2021/01 
RT-ddPCR (N1, N2) PMMoV, HF183 Correlation (Confirmed daily cases)  ✓  ✓ 
Giraud-Billoud et al. (2021)  AR (Mendoza) 2 WWTPs 1,000,000 2020/07
2020/11 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases, mortality) ✓ ✓ ✓  3–6 
Hoar et al. (2022)  US (New York) 14 WWTPs 8,600,000 2020/08
2021/04 
RT-qPCR, RT-ddPCR (N1) Flow Rate Correlation (Confirmed daily cases, hospitalizations)  ✓ ✓  no 
Isaksson et al. (2022)  SE (Luleå) 2 WWTPs 70,000 2021/01
2021/03 
RT-qPCR (N1) PMMoV, Flow Rate Correlation (Confirmed daily cases)  ✓   0–8 
Lara-Jacobo et al. (2022)  CA (Ontario) 2 WWTPs 300,000 2020/10
2021/04 
LC-MS/MS, RT-qPCR (N1) Flow Rate Correlation (Confirmed daily cases)  ✓   5–6 
Lazuka et al. (2021)  FR (10 cities in FR) 10 WWTPs 3,250,000 2020/07

2020/12 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases, hospitalizations, mortality)  ✓   NR 
Li et al. (2022bUS (Nevada) 3 WWTPs 390,000 2020/07
2021/09 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases) ✓ ✓   
Monteiro et al. (2022)  PT (2 areas PT) 5 WWTPs, 3 SPs 2,000,000 2020/04
2020/12 
RT-qPCR (N, E, RdRp) Flow Rate Correlation (hospitalizations) ✓ ✓   NR 
Nagarkar et al. (2022)  US (Cincinnati) 2 WWTPs 520,000 2020/05
2020/10 
RT-ddPCR (N1, N2) crAssphage, PMMoV, HF183 Correlation (Confirmed daily cases)  ✓  ✓ 7–14 
Nelson et al. (2022)  US (Austin) 2 WWTPs 1,800,000 2020/05
2022/01 
RT-qPCR (N2) Flow Rate Correlation (Confirmed daily cases)  ✓   1–7 
Padilla-Reyes et al. (2022)  MX (Monterrey) 4 WWTPs, 4 SPs 3,800,000 2020/06
2020/12 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases, mortality)  ✓   2–7 
Robotto et al. (2021)  IT (Piedmont) 4 WWTPs (out of 13) 1,700,000 2020/08
2021/03 
RT-qPCR (N1, N2, E) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  Few days 
Rothman et al. (2021)  US (South California) 7 WWTPs 16,000,000 2020/08
2021/01 
RT-ddPCR (N1) no Correlation (Confirmed daily cases)  ✓ ✓  NR 
Sangsanont et al. (2022)  TH (Bangkok) 19 WWTPs 2,750,000 2021/01
2021/04 
RT-qPCR (N1, N2) CrAssphage Correlation (Confirmed daily cases)  ✓ ✓  22–24 
Stephens et al. (2022)  NL (2 cities in NL) 2 WWTPs 900,000 2020/03
2021/05 
RT-qPCR (N1, N2, N3, E) CrAssphage Correlation (Confirmed daily cases, hospitalizations)  ✓   3–9 
Street et al. (2021)  ZA (Cape Town) 23 WWTPs 4,000,000 2020/07
2020/08 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Tandukar et al. (2022)  NP (Kathmandu Valley) 2 WWTPs, NR 2020/07
2021/02 
RT-qPCR (N1, N2) CrAssphage Correlation (Confirmed daily cases)  ✓ ✓  NR 
Tanimoto et al. (2022)  JP (Kobe) 2 WWTPs 780,000 2021/02
2021/10 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases) ✓  ✓  NR 
Tiwari et al. (2022)  FI (28 locations in FI) 28 WWTPs 3,300,000 2020/08
2021/05 
RT-qPCR (N2) Flow Rate, crAssphage Correlation (Confirmed daily cases)   ✓  NR 
Tomasino et al. (2021)  PT (Porto) 2 WWTPs 370,000 2020/05
2021/03 
RT-qPCR (N1, N2) no Correlation (Confirmed daily cases) ✓ ✓ ✓  NR 
Wang et al. (2021)  US (Los Angeles) 5 WWTPs 8,900,000 2020/05
2021/03 
RT-qPCR (N1, N2, E) Flow Rate Correlation (Confirmed daily cases)  ✓   
Wu et al. (2022bUS (Massachusetts) 1 WWTP 2,300,000 2020/01
2020/05 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases)  ✓ ✓  4–10 
Wu et al. (2021)  US (40 States in US) 353 SPs 42,500,000 2020/02
2020/06 
RT-qPCR (N1, N2) PMMoV Correlation (Confirmed daily cases, mortality)  ✓ ✓  NR 
Xiao et al. (2022)  US (Massachusetts) 1 WWTP 2,300,000 2020/03
2021/05 
RT-qPCR (N1, N2) PMMoV Modeling (Confirmed daily cases) ✓ ✓ ✓  1–6 
Zhan et al. (2022)  US (Miami) 3 WWTP, 1 SP 2,500,000 2020/09
2021/11 
RT-qPCR (N1, ORF1ab) PMMoV Correlation (Confirmed daily cases, hospitalizations)  ✓ ✓  0–7 
Zhao et al. (2022)  US (Detroit) 3 WWTPs 2,800,000 2020/09
2021/08 
RT-qPCR (N1, N2) Flow Rate Correlation (Confirmed daily cases) ✓ ✓  ✓ 14–35 
Zheng et al. (2022)  HK (Hong Kong) 3 WWTPs 2,100,000 2020/12
2021/06 
RT-qPCR (N1, E) PMMoV, Flow Rate Correlation (Confirmed daily cases) ✓ ✓ ✓  NR 
Anneser et al. (2022)  US (Massachusetts) 5 WWTPs 2,600,000 2020/08
2021/03 
RT-qPCR (N1, N2, N3) PMMoV Modeling (Confirmed daily cases)  ✓ ✓  NR 
Claro et al. (2021)  BR (Sao Paulo) 2 WWTPs, 3 SPs 1,400,000 2020/06
2021/04 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  14 
Cluzel et al. (2022)  FR (All FR) 168 WWTPs 12,800,000 2020/03
2021/05 
RT-qPCR, RT-ddPCR (E, RdRp) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  
Fitzgerald et al. (2021)  GB (Scotland) 28 WWTPs 2,600,000 2020/05
2021/01 
RT-qPCR (N1, E) Flow Rate Modeling (Confirmed daily cases, mortality) ✓ ✓ ✓  NR 
Galani et al. (2022)  GR (Attica) 1 WWTP 3,700,000 2020/09
2021/03 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases, hospitalizations)  ✓ ✓  5–9 
Greenwald et al. (2021)  US (San Francisco) 6 SPs 2,600,000 2020/04
2020/09 
RT-qPCR (N1) PMMoV, crAssphage Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ 0–21 
Huisman et al. (2022)  CH (Zurich, San Jose) 2 WTPs 1,500,000 2020/11
2021/03 
RT-qPCR, RT-ddPCR (N1, N2) Flow Rate/PMMoV Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓ ✓ 1–4 
Koureas et al. (2021)  GR (Larissa and Volos) 2 WWTPs 150,000 2020/10
2021/04 
RT-qPCR (ORF1ab) Ammonium, COD, BOD, TSS Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Krivonakova et al. (2021)  SK (Bratislava) 2 WWTPs 575,000 2020/09
2021/03 
RT-qPCR (ORF1ab, S, E, RdRp) no Modeling (Confirmed daily cases, mortality)  ✓ ✓  12–26 
Morvan et al. (2022)  GB (All England) 45 SPs 17,300,000 2020/07
2021/03 
RT-qPCR (N1, E) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓ ✓ 4–5 
Nourbakhsh et al. (2022)  CA (3 cities in CA) 6 WWTPs 3,500,000 2021/03
2021/06 
RT-qPCR (N1, N2) PMMoV Modeling (Confirmed daily cases, hospitalizations)  ✓ ✓ ✓ NR 
Vallejo et al. (2022)  ES (A Coruña) 1 WWTP, 1 SP 370,000 2020/04
2020/06 
RT-qPCR (N) Flow Rate Modeling (Confirmed daily cases) ✓ ✓ ✓ ✓ NR 
Amereh et al. (2022)  IR (Tehran) 7 WWTPs 5,600,000 2020/09
2021/04 
RT-qPCR (N, ORF1ab) Flow Rate Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  1–2 
de Sousa et al. (2022)  BR (Goiás) 1 WWTP 700,000 2020/01
2020/08 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases, hospitalizations, mortality)  ✓ ✓  NR 
Jiang et al. (2022)  US (Utah) 47WWTPs 2,500,000 2020/05
2021/07 
RT-qPCR (N1, N2) Flow Rate Modeling (Confirmed daily cases)  ✓ ✓  2–4 
Camphor et al. (2022)  AU (New South Wales) 7 WWTPs 3,793,038 2020/03
2020/07 
RT-qPCR (N1, N2) no Modeling (Confirmed daily cases)    ✓ NR 
Li et al. (2023)  CA (Alberta) 12 WWTPS 3,500,000 2020/05
2021/06 
RT-qPCR (N1, N2) no Modeling (Confirmed daily cases) ✓  ✓  NR 
Wolfe et al. (2021)  US (California) 8 WWTPs 4,000,000 2020/11
2021/03 
RT-ddPCR (N, S, ORF1ab) PMMoV Correlation (Confirmed daily cases)  ✓ ✓ ✓ NR 

SPs, sampling points; W, water, wastewater, and sanitation agencies; E, environmental research institutions; H, public health research institutions; P, public health authorities; NR, not reported.

A total of 27 publications included in the evaluation of the effectiveness of EWS were performed in targeted settings, such as campuses/schools (n = 17), neighborhoods (n = 4), hospitals (n = 2), public areas (n = 2), quarantine facilities (n = 1) or aircraft (n = 1) (Table 3). The studies performed at campus level were designed to provide actionable information to the university administration after localized outbreaks (Brooks et al. 2021; Corchis-Scott et al. 2021; Reeves et al. 2021; Bitter et al. 2022; Kotay et al. 2022), provide evidence for the application of ES (Karthikeyan et al. 2021; Cavany et al. 2022; Cohen et al. 2022; de Llanos et al. 2022; Lu et al. 2022; Wright et al. 2022; Zambrana et al. 2022) or assess the impact of vaccination campaigns (Bivins & Bibby 2021). The studies of ES of SARS-CoV-2 at neighborhood, public area, hospital or quarantine facility level facilitated identifying the increase of infection at a high granular level using decentralized monitoring of wastewater through the sewer system (Mota et al. 2021; Acosta et al. 2022; Deng et al. 2022a; Hewitt et al. 2022) and assessing the implementation of wastewater-initiated public health interventions (de Araujo et al. 2022; Deng et al. 2022b; Zdenkova et al. 2022).

Table 3

Synthesis of publications included to evaluate the effectiveness of ES as an EWS for COVID-19 at hotspot level (n = 27)

IDCountryWWTPs or SPsPopulationStudy periodDetection methodNormalizationStudy locationCo-authors affiliation
EW window
(Location)(Target genes)(Clinical data)WEHP(days)
Sharkey et al. (2021)  US (Miami) 3 SPs 6,000 2020/08
2020/12 
RT-qPCR (N1, N2) NR Campus/School (Confirmed daily cases)  ✓ ✓  NR 
Ahmed et al. (2022cAU (Northern Territory) 37 SPs 6,570 2020/12
2021/03 
RT-qPCR (N1, N2, N) no Aircraft (Confirmed daily cases)  ✓   NR 
Acosta et al. (2021)  CA (Calgary) 3 SPs 2100 2020/08
2020/12 
RT-qPCR (N1, N2, E) PMMoV Hospital (Hospitalizations) ✓ ✓ ✓ ✓ 12 
Hinz et al. (2022)  CA (Ontario) 2 SPs NR 2020/09
2020/12 
RT-qPCR (N1, N2) NA Hospital (Confirmed daily cases)  ✓ ✓  
Acosta et al. (2022)  CA (Calgary) 3 WWTPs 1,500,000 2020/06
2021/06 
RT-qPCR (N1, N2) Flow Rate Neighborhood (Confirmed daily cases) ✓ ✓ ✓ ✓ 12 
Deng et al. (2022aHK (Hong Kong) 112 SPs 5,300,000 2020/12
2021/06 
RT-qPCR (N1, E) no Neighborhood (Confirmed daily cases) ✓ ✓ ✓ ✓ 
Mota et al. (2021)  BR (Belo Horizonte) 2 WWTPs, 15 SPs 2,200,000 2020/05
2020/08 
RT-qPCR (N1) COD Neighborhood (Confirmed daily case, hospitalizations) ✓ ✓  ✓ NR 
de Araujo et al. (2022)  BR (Belo Horizonte) 2 WWTPs, 5 SPs 2,200,000 2020/09
2021/01 
RT-qPCR (N1) no Public Areas (Confirmed daily case, hospitalizations) ✓ ✓  ✓ 14 
Zdenkova et al. (2022)  CZ (Prague) 14 SPs 1,500,000 2020/08
2021/05 
RT-qPCR (N1, RdRp, S) Flow Rate Public Areas (Confirmed daily case) ✓ ✓ ✓ ✓ 7–14 
Bitter et al. (2022)  CA (1 city CA) 2 SPs NR 2020/09
2021/08 
RT-qPCR (N1, N2) PMMoV Campus/School (Confirmed daily case)  ✓   NR 
Bivins & Bibby (2021)  US (Notre Dame) 1 SP 6,358 2021/04
2021/05 
RT-ddPCR (N1) PMMoV Campus/School (Confirmed daily case)  ✓   NR 
Brooks et al. (2021)  US (Main) 6 SPs 605 2020/08
2020/11 
RT-qPCR (N) no Campus/School (Confirmed daily case)   ✓ ✓ 
Cavany et al. (2022)  US (NR) 1 SP 7,000 2020/08
2020/11 
RT-ddPCR (N1) NR Campus/School (Confirmed daily case)  ✓   NR 
Cohen et al. (2022)  US (Virginia) 17 SPs 2,000 2020/09
2021/05 
RT-qPCR, RT-ddPCR (N, E, S) NR Campus/School (Confirmed daily case)  ✓ ✓  
Corchis-Scott et al. (2021)  CA (Windsor) 5 WWTPs, 1 SP NR 2021/02
2021/04 
RT-qPCR (N1) PMMoV Campus/School (Confirmed daily case)  ✓ ✓ ✓ 
de Llanos et al. (2022)  ES (Castellon) 12 SPs 15,000 2020/10
2021/07 
RT-qPCR (N1, E) NR Campus/School (Confirmed daily case)   ✓ ✓ NR 
Karthikeyan et al. (2021)  US (California) 239 SPs 7,614 2020/11
2020/12 
RT-qPCR (N1, N2, E) PMMoV Campus/School (Confirmed daily case)  ✓ ✓  
Kotay et al. (2022)  US (Virginia) 16 SPs 2,000 2020/09
2021/05 
RT-qPCR (N1, N2) NR Campus/School (Confirmed daily case)  ✓ ✓  NR 
Lu et al. (2022)  US (Ohio) 6 SPs 7,767 2020/08
2020/12 
RT-ddPCR (N1, N2, E) PMMoV, crAssphage Campus/School (Confirmed daily case)  ✓ ✓  NR 
Reeves et al. (2021)  US (Colorado) 20 SPs 6,200 2020/08
2020/11 
RT-qPCR (N1, N2) PMMoV, F + bacteriophage Campus/School (Confirmed daily case)  ✓ ✓  NR 
Wright et al. (2022)  US (Southwestern US) 2 SPs 60,000 2020/08
2021/01 
RT-qPCR (E) Flow Rate Campus/School (Confirmed daily case)  ✓   NR 
Zambrana et al. (2022)  US (Stanford) 3 SPs 10,700 2020/01
2021/04 
RT-ddPCR (N1, N2) PMMoV Campus/School (Confirmed daily case)  ✓ ✓  NR 
Deng et al. (2022bHK (Hong Kong) 492 SPs 35,040 2020/12
2021/03 
RT-qPCR (N1, E) no Neighborhood (Confirmed daily case, hospitalizations) ✓ ✓ ✓  
Wang et al. (2022aUS (Atlanta) 25 SPs 15,398 2020/08
2021/03 
RT-qPCR (N1) no Campus/School (Confirmed daily case)   ✓ ✓ 7–14 
Welling et al. (2022)  US (North Carolina.) 5 SPs 600 2021/02
2021/05 
RT-qPCR (N1) no Campus/School (Confirmed daily case)  ✓ ✓  NR 
Godinez et al. (2022)  US (New York) 12 SPs 5,700 2020/09
2020/11 
RT-qPCR (RdRp) CrAssphage Campus/School (Confirmed daily case)  ✓ ✓  
Hewitt et al. (2022)  NZ (Auckland) 2 SPs 120,000 2020/07
2020/11 
RT-qPCR (N) no Quarantine facility (Confirmed daily case) ✓ ✓   NR 
IDCountryWWTPs or SPsPopulationStudy periodDetection methodNormalizationStudy locationCo-authors affiliation
EW window
(Location)(Target genes)(Clinical data)WEHP(days)
Sharkey et al. (2021)  US (Miami) 3 SPs 6,000 2020/08
2020/12 
RT-qPCR (N1, N2) NR Campus/School (Confirmed daily cases)  ✓ ✓  NR 
Ahmed et al. (2022cAU (Northern Territory) 37 SPs 6,570 2020/12
2021/03 
RT-qPCR (N1, N2, N) no Aircraft (Confirmed daily cases)  ✓   NR 
Acosta et al. (2021)  CA (Calgary) 3 SPs 2100 2020/08
2020/12 
RT-qPCR (N1, N2, E) PMMoV Hospital (Hospitalizations) ✓ ✓ ✓ ✓ 12 
Hinz et al. (2022)  CA (Ontario) 2 SPs NR 2020/09
2020/12 
RT-qPCR (N1, N2) NA Hospital (Confirmed daily cases)  ✓ ✓  
Acosta et al. (2022)  CA (Calgary) 3 WWTPs 1,500,000 2020/06
2021/06 
RT-qPCR (N1, N2) Flow Rate Neighborhood (Confirmed daily cases) ✓ ✓ ✓ ✓ 12 
Deng et al. (2022aHK (Hong Kong) 112 SPs 5,300,000 2020/12
2021/06 
RT-qPCR (N1, E) no Neighborhood (Confirmed daily cases) ✓ ✓ ✓ ✓ 
Mota et al. (2021)  BR (Belo Horizonte) 2 WWTPs, 15 SPs 2,200,000 2020/05
2020/08 
RT-qPCR (N1) COD Neighborhood (Confirmed daily case, hospitalizations) ✓ ✓  ✓ NR 
de Araujo et al. (2022)  BR (Belo Horizonte) 2 WWTPs, 5 SPs 2,200,000 2020/09
2021/01 
RT-qPCR (N1) no Public Areas (Confirmed daily case, hospitalizations) ✓ ✓  ✓ 14 
Zdenkova et al. (2022)  CZ (Prague) 14 SPs 1,500,000 2020/08
2021/05 
RT-qPCR (N1, RdRp, S) Flow Rate Public Areas (Confirmed daily case) ✓ ✓ ✓ ✓ 7–14 
Bitter et al. (2022)  CA (1 city CA) 2 SPs NR 2020/09
2021/08 
RT-qPCR (N1, N2) PMMoV Campus/School (Confirmed daily case)  ✓   NR 
Bivins & Bibby (2021)  US (Notre Dame) 1 SP 6,358 2021/04
2021/05 
RT-ddPCR (N1) PMMoV Campus/School (Confirmed daily case)  ✓   NR 
Brooks et al. (2021)  US (Main) 6 SPs 605 2020/08
2020/11 
RT-qPCR (N) no Campus/School (Confirmed daily case)   ✓ ✓ 
Cavany et al. (2022)  US (NR) 1 SP 7,000 2020/08
2020/11 
RT-ddPCR (N1) NR Campus/School (Confirmed daily case)  ✓   NR 
Cohen et al. (2022)  US (Virginia) 17 SPs 2,000 2020/09
2021/05 
RT-qPCR, RT-ddPCR (N, E, S) NR Campus/School (Confirmed daily case)  ✓ ✓  
Corchis-Scott et al. (2021)  CA (Windsor) 5 WWTPs, 1 SP NR 2021/02
2021/04 
RT-qPCR (N1) PMMoV Campus/School (Confirmed daily case)  ✓ ✓ ✓ 
de Llanos et al. (2022)  ES (Castellon) 12 SPs 15,000 2020/10
2021/07 
RT-qPCR (N1, E) NR Campus/School (Confirmed daily case)   ✓ ✓ NR 
Karthikeyan et al. (2021)  US (California) 239 SPs 7,614 2020/11
2020/12 
RT-qPCR (N1, N2, E) PMMoV Campus/School (Confirmed daily case)  ✓ ✓  
Kotay et al. (2022)  US (Virginia) 16 SPs 2,000 2020/09
2021/05 
RT-qPCR (N1, N2) NR Campus/School (Confirmed daily case)  ✓ ✓  NR 
Lu et al. (2022)  US (Ohio) 6 SPs 7,767 2020/08
2020/12 
RT-ddPCR (N1, N2, E) PMMoV, crAssphage Campus/School (Confirmed daily case)  ✓ ✓  NR 
Reeves et al. (2021)  US (Colorado) 20 SPs 6,200 2020/08
2020/11 
RT-qPCR (N1, N2) PMMoV, F + bacteriophage Campus/School (Confirmed daily case)  ✓ ✓  NR 
Wright et al. (2022)  US (Southwestern US) 2 SPs 60,000 2020/08
2021/01 
RT-qPCR (E) Flow Rate Campus/School (Confirmed daily case)  ✓   NR 
Zambrana et al. (2022)  US (Stanford) 3 SPs 10,700 2020/01
2021/04 
RT-ddPCR (N1, N2) PMMoV Campus/School (Confirmed daily case)  ✓ ✓  NR 
Deng et al. (2022bHK (Hong Kong) 492 SPs 35,040 2020/12
2021/03 
RT-qPCR (N1, E) no Neighborhood (Confirmed daily case, hospitalizations) ✓ ✓ ✓  
Wang et al. (2022aUS (Atlanta) 25 SPs 15,398 2020/08
2021/03 
RT-qPCR (N1) no Campus/School (Confirmed daily case)   ✓ ✓ 7–14 
Welling et al. (2022)  US (North Carolina.) 5 SPs 600 2021/02
2021/05 
RT-qPCR (N1) no Campus/School (Confirmed daily case)  ✓ ✓  NR 
Godinez et al. (2022)  US (New York) 12 SPs 5,700 2020/09
2020/11 
RT-qPCR (RdRp) CrAssphage Campus/School (Confirmed daily case)  ✓ ✓  
Hewitt et al. (2022)  NZ (Auckland) 2 SPs 120,000 2020/07
2020/11 
RT-qPCR (N) no Quarantine facility (Confirmed daily case) ✓ ✓   NR 

SPs, sampling points; W, water, wastewater, and sanitation agencies; E, environmental research institutions; H, public health research institutions; P, public health authorities; NR, not reported.

Reporting of timeliness

Timeliness was defined as the ability of the ES system to provide an EWS compared to other surveillance systems. The clinical data used for the comparison with wastewater data were confirmed daily cases (n = 90), hospitalizations (n = 16) and mortality numbers (n = 11). Based on this screening, 47 publications (51%) reported that ES of SARS-CoV-2 could be an EWS preceding clinical data by approximately 1–2 weeks [median of 8 days, interquartile range (IQR) 5.5–14]. Four studies did not report the exact number of days. Ten studies reported that clinical data were preceding wastewater results while 31 studies did not report or include any information on timeliness. However, very few studies reported the timeliness of results in real-time, a critical piece of information for the evaluation of the timeliness. Most of the claims on the EWS capability of ES were based on retrospective analysis.

Reporting of sensitivity and specificity

Eight studies performed a sensitivity or specificity assessment for ES of SARS-CoV-2 in wastewater. These studies tested the ability of ES to correctly identify SARS-CoV-2 viral copies in wastewater in specific sampling sites (i.e., buildings) with or without clinically confirmed cases. Five of these studies (Deng et al. 2022b; Godinez et al. 2022; Hewitt et al. 2022; Wang et al. 2022a; Welling et al. 2022) performed the evaluation under highly controlled conditions at targeted areas, such as the large-scale program performed in local communities in Hong Kong (Deng et al. 2022b). These studies reported different levels of sensitivity based on their study design: 54% (Deng et al. 2022b), 45% (Wang et al. 2022a), 43% (Welling et al. 2022), or 73–95% (Godinez et al. 2022), and 90–100% for a prevalence of 0.03% (Hewitt et al. 2022). Two of these studies also reported on specificity: 95% (Deng et al. 2022b), and 86% (Welling et al. 2022).

The three remaining studies were performed at larger catchment areas at population level, such as the epidemiological analysis on wastewater surveillance and case notification data performed in South Wales (Australia) reporting a sensitivity of 44% and specificity of 88% (Camphor et al. 2022), a sensitivity assessment performed in Alberta (Canada) reporting a sensitivity of 99% for a prevalence of 0.038% (Li et al. 2023), or a study in California (United States) reporting an average estimated incidence rate detection limit of 0.0014% (Wolfe et al. 2021) (Table 3).

Early detection of new variants

Of the 65 publications on the detection of variants, 82% (n = 53) compared SARS-CoV-2 sequences detected in wastewater with those found in clinical samples (Table 4). The most predominant geographical areas for these publications were Europe with 48% (Figure 1), including a pan-European study with 54 European cities (Agrawal et al. 2022a), followed by North America with 29% and Asia with 17%. Two studies were published in Oceania, one in Africa and one in South America. The United States, with 14 articles, was the country with the highest number of publications on variants included in this systematic review.

Table 4

Synthesis of publications included to assess the ability to detect the early introduction of new variants into wastewater (n = 65)

IDCountry (Location)WWTPs or SPsStudy TypeStudy PeriodVariant Detection MethodGenes Tested for Variant DetectionNGS PlatformaNGS PrimersVariant ReportingEarly Warning WindowVariantsb
Agrawal et al. (2022a)  Pan-European (20 Countries) 54 WWTPs Population Level 2021/03
2021/03 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Agrawal et al. (2022b)  DE (Frankfurt) 1 WWTP,
1 SP 
Population Level,
Hotspot (Airport) 
2021/11
2021/11 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Detection • BA 
Ahmed et al. (2022b)  AU (Eaton) 12 SP Hotspot (Airport) 2021/04
2021/08 
Targeted NGS
RT-PCR 
Nanopore, Atoplex ARTIC V3
ATOPlex SARS-CoV-2 full-length genome panel 
Proportion • BA 
Ai et al. (2021)  US (Ohio) 9 WWTPs Population Level 2020/07
2021/01 
Targeted NGS – Illumina CovidSeq Proportion • B.1.1.7
• Other 
Amman et al. (2022)  AT (National) 94 WWTPs Population Level 2020/12
2022/02 
Targeted NGS – Illumina ARTIC VarSkip 1a Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
Avgeris et al. (2021)  GR (Athens) 1 WWTP Population Level 2020/09
2020/11 
Targeted NGS S
3a
1ab 
Ion Torrent In-house Primers Proportion – • B.1.1.7 
Bagutti et al. (2022)  CH (Basel) 1 WWTP Population Level 2021/07
2021/12 
Targeted NGS – Illumina ARTIC V4 Proportion • B.1.617.2
• BA 
Bar-Or et al. (2022)  IL (National) 13 WWTP Population Level 2020/12
2021/03 
Targeted NGS  Illumina Illumina COVID-seq kit. ARTIC V4 Proportion – • B.1.1.7 
Bar-Or et al. (2021)  IL (National) 9 WWTPs Population Level 2020/08
2021/02 
Targeted NGS – Illumina ARTIC Proportion • B.1.1.7
• B.1.351
• P.1
• Other 
Boehm et al. (2022)  US (California) 8 WWTPs Population Level 2022/01
2022/04 
ddRT-PCR N
– – Proportion 10.5 • BA 
Brumfield et al. (2022)  US (Maryland) 1 SP Hotspot (Neighbourhood) 2020/12
2021/11 
WGS;
Targeted NGS 
– Illumina Swift Normalase Amplicon SARS-CoV-2 Panel kit Proportion – • B.1.1.7
• B.1.351 
Caduff et al. (2022)  CH (Zurich) 1 WWTP Population Level 2020/12
2022/03 
RT-ddPCR S
ORF1a 
– – Proportion 7.3 • B.1.1.7
• B.1.351
• P.1 
Carcereny et al. (2022)  ES (Catalonia) 14 WWTP Population Level 2020/11
2021/04 
RT-qPCR;
Targeted NGS 
Illumina ARTIC V3 Proportion – • B.1.1.7 
Carcereny et al. (2021)  ES (National) 32 WWTPs Population Level 2020/12
2021/03 
RT-qPCR;
Targeted NGS 
Illumina ARTIC V3 Proportion – • B.1.1.7 
Chassalevris et al. (2022)  GR (Thessaloniki) 1 WWTP Population Level 2021/11
2022/01 
RT-qPCR – – Proportion • BA 
Cutrupi et al. (2022)  IT (Trento) 2 WWTPs Population Level 2020/12
2022/04 
RT-qPCR;
Sanger sequencing;
Targeted NGS 
Nanopore – Detection • B.1.617.2
• BA 
Dharmadhikari et al. (2022)  IN (Pune) 2 WWTPs Population Level 2020/12
2021/03 
Targeted NGS – Nanopore nCoV-2019 sequencing protocol v3 (LoCost) V3 Detection NR • B.1.617.2 
El-Malah et al. (2022)  QA (National) 5 WWTPs Population Level 2021/03
2021/04 
Targeted NGS – Illumina CleanPlex SARS-CoV-2 Panel Proportion – • B.1.1.7
• B.1.351
• B.1.617.2 
Graber et al. (2021)  CA (Ottawa) 2 WWTPs Population Level 2021/01
2021/04 
RT-qPCR – – Proportion – • B.1.1.7 
Heijnen et al. (2021)  NL (Amsterdam, Utrecht) 2 WWTPs Population Level 2020/11
2021/03 
RT-ddPCR – – Proportion – • B.1.1.7
• B.1.351 
Herold et al. (2021)  LU (Luxembourg) 13 WWTPs Population Level 2020/03
2021/03 
Targeted NGS – Illumina ARTIC V1 Proportion – • B.1.1.7
• B.1.351
• P.1 
Ho et al. (2022)  DE (Karlsruhe) 1 WWTP Population Level 2020/06
2021/07 
RT-ddPCR S (N501Y) – – Proportion – • B.1.1.7 
Hubert et al. (2022)  CA (Alberta) 24 WWTPs Population Level 2021/11
2022/01 
RT-qPCR – – Proportion • B.1.617.2
• BA 
Jahn et al. (2022)  CH (National) 2 WWTPs,
1 SP 
Population Level,
Hotspot (Ski Resort) 
2020/07
2021/09 
Targeted NGS – Illumina ARTIC v3/v4 Proportion 65.5 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Johnson et al. (2022)  ZA (Cape Town) 24 WWTPs Population Level 2021/05
2021/07 
RT-qPCR, Targeted NGS DNBSEQ ATOPlex for SARS-CoV-2 fragments Proportion – • B.1.1.7
• B.1.351
• B.1.617.2 
Joshi et al. (2022)  IN (Ahmedabad) 1 WWTP Population Level 2020/11
2021/02 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Detection 30 • B.1.617.2 
Karthikeyan et al. (2022)  US
(San Diego) 
1 WWTP,
131 SPs 
Population Level,
Hotspot (Campus) 
2020/11
2021/09 
Targeted NGS
RT-qPCR 
c Illumina COVG1V2-96 Proportion 14 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
La Rosa et al. (2021)  IT (Latina Province) 3 WWTPs Population Level 2021/04
2021/05 
RT-nested-PCR, WGS Nanopore – Proportion – • B.1.1.7
• P.1
• Other 
La Rosa et al. (2022)  IT (National) 134 WWTPs Population Level 2021/11
2021/12 
RT-qPCR and Sanger sequencing – – Proportion – • BA 
Layton et al. (2022)  US (Oregon city) 6 WWTPs,
22 SPs 
Population Level 2020/04
2021/05 
Targeted NGS – Illumina Swift Amplicon SARS-CoV-2 Panel Proportion – • NR 
Lee et al. (2022)  IT (Brescia) 1 WWTP Population Level 2021/04
2022/01 
RT-qPCR – – Proportion – • B.1.617.2
• BA 
Lee et al. (2021)  US (11 US States) 16 WWTPs Population Level 2020/10
2021/03 
RT-qPCR – – Proportion – • B.1.1.7 
Li et al. (2022c)  US (Nevada) 3 WWTPs Population Level 2020/11
2021/06 
Targeted NGS – Illumina Mybait probe enrichment Proportion – • B.1.1.7
• P.1
• B.1.617.2
• Other 
Markt et al. (2022)  LI (National) 1 WWTP Population Level 2020/09
2021/03 
Targeted NGS – Illumina ARTIC V3 Proportion 21 • B.1.1.7 
Masachessi et al. (2022)  AR
(Córdoba) 
4 WWTPs Population Level 2020/05
2021/08 
RT-qPCR – – Detection – • B.1.1.7
• P.1
• B.1.617.2 
Nag et al. (2022)  IN (Jaipur) 11 WWTPs Population Level 2021/02
2021/06 
Targeted NGS – Illumina NGSeq ARTIC Sars-CoV-2 kit Detection – • B.1.617.2 
Novoa et al. (2022)  ES (Galicia) 11 WWTPs Population Level 2020/05
2021/05 
Targeted NGS – Illumina ARTIC V3 Proportion – • B.1.1.7
• B.1.351
• P.1
• Other 
Oloye et al. (2022)  CA (Prince Albert)  3 WWTPs Population Level 2021/08
2022/01 
RT-qPCR Targeted NGS S
Illumina ARTIC V3 Proportion – • B.1.617.2
• BA 
Pechlivanis et al. (2022)  GR (Thessaloniki) 1 WWTP Population Level 2020/12
2021/04 
Targeted NGS – Illumina ARTIC V3 Proportion – • B.1.1.7
• B.1.351 
Perez-Cataluna et al. (2022)  ES (National) 14 WWTPs Population Level 2020/04
2021/01 
Targeted NGS – Illumina ARTIC V3 Proportion 42 • B.1.1.7 
Radu et al. (2022)  AT (Vienna) 1 WWTP Population Level 2020/10
2021/05 
RT-qPCR Targeted NGS Illumina ARTIC V3 Proportion 14 • B.1.1.7
• P.1 
Reynolds et al. (2022)  IE (Dublin) 1 WWTP Population Level 2020/06
2021/08 
RT-ddPCR – – Proportion 21 • B.1.1.7
• B.1.617.2 
Rios et al. (2021)  FR (Nice) 1 WWTPs,
20 SPs 
Population Level 2020/10
2021/03 
Targeted NGS – Nanopore ARTIC v3 Proportion – • B.1.1.7
• B.1.351
• P.1
• Other 
Roka et al. (2022)  HU (National) 22 WWTPs Population Level 2020/12
2021/03 
RT-ddPCR Melting Curve Genotyping – – Proportion – • B.1.1.7 
Rouchka et al. (2021)  US (Louisville) 5 WWTPs Population Level 2020/10
2021/01 
Targeted NGS – Illumina Swift Normalase Amplicon Panel (SNAP) SARS-CoV-2 Proportion 21 • B.1.1.7
• B.1.351
• P.1
• Other 
Smyth et al. (2022)  US (New York City) 14 WWTPs Population Level 2021/01
2021/06 
Targeted NGS – Illumina S gene specific primers Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• Other 
Sutton et al. (2022)  US (Oregon) 3 WWTPs Population Level 2021/03
2021/04 
Targeted NGS – Illumina Swift Amplicon SARS-CoV-2 Panel Proportion 12 • B.1.351 
Swift et al. (2021)  US (South Carolina) 2 WWTPs Population Level 2020/07
2021/01 
Targeted NGS – Nanopore ARTIC V3 Detection – • B.1.351
• P.1
• B.1.617.2
• Other 
Vo et al. (2022)  US (Las Vegas) 7 WWTPs Population Level 2020/03
2021/04 
Targeted NGS – Illumina CleanPlex SARS-CoV-2 FLEX Panel Proportion 30 • B.1.1.7
• Other 
Wang et al. (2022b)  AU (Queensland) 18 WWTPs,
1 SP 
Population Level, Hotspot (Aircraft) 2021/08
2021/09 
Targeted NGS – DNBSEQ ATOPlex Detection – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
RT-qPCR – – Detection – • BA 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
RT-dPCR N
– – Proportion  • BA 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 research panel Detection – • BA 
Wolfe et al. (2022)  US (California) 1 WWTP Population Level 2020/08
2022/01 
RT-ddPCR S
ORF3a 
– – Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
Wurtz et al. (2021) e FR (Marseille) 2 WWTPs Population Level 2021/04
2021/04 
RT-qPCR – – Proportion – • B.1.1.7 
Wurtz et al. (2021) e FR (Marseille) 2 WWTPs Population Level 2021/04
2021/04 
Targeted NGS – Illumina TIC nCoV-2019 V3 Panel (LoCost ARTIC) Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• Other 
Wurtzer et al. (2022)  FR (Paris) 5 WWTPs Population Level 2020/03
2021/06 
RT-qPCR – – Proportion • B.1.1.7 
Xie et al. (2022)  CA (Saskatoon City) 1 WWTP Population Level 2020/07
2021/08 
RT-ddPCR N
– – Proportion 10.5 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Xu et al. (2022)  HK (Hong Kong) 1 SPs Hotspot (Hotel) NR WGS
Targeted NGS 
Nanopore ARTIC V3 Detection • B.1.351
• B.1.617.2
• BA 
Yaniv et al. (2021)  IL (2 cities) 1 WWTP,
12 SPs 
Population Level 2020/11
2021/10 
RT-qPCR – – Detection – • B.1.1.7
• B.1.617.2 
Yaniv et al. (2022)  IL (Beer-Sheva) 1 WWTP Population Level 2021/12
2022/01 
RT-qPCR – – Proportion • B.1.617.2
• BA 
Yu et al. (2022)  US (California) 2 SPs Population Level 2020/07
2021/08 
RT-ddPCR – – Proportion 14 • B.1.1.7
• B.1.617.2 
Rubio-Acero et al. (2021)  DE (Munich) 6 WWTPs Population Level 2020/04
2021/04 
Targeted NGS – Illumina ARTIC Proportion 14 • B.1.1.7
• B.1.351
• P.1 
Tandukar et al. (2022)  NP (Kathmandu Valley) 2 WWTPs, 1 SP Population Level
Hotspot (hospital) 
2020/07
2021/02 
qRT-PCR N
– – Detection – • B.1.1.7 
Galani et al. (2022)  GR (Attica) 1 WWTP Population Level 2020/08
2021/03 
Targeted NGS – Ion Torrent – Proportion • B.1.1.7 
Deng et al. (2022a)  HK (Hong Kong) 112 SPs Hotspot (Neighbourhood) 2020/12
2021/06 
RT-qPCR
Targeted NGS 
Illumina ARTIC Detection • B.1.617.2 
Corchis-Scott et al. (2021)  CA (Windsor) 1 WWTPs Hotspot (Campus) 2021/02
2021/03 
RT-qPCR – – Detection • B.1.1.7
• Other 
de Llanos et al. (2022)  ES (Castellon) 12 SPs Hotspot (Campus) 2020/10
2021/07 
Targeted NGS – Illumina ARTIC Detection – • B.1.617.2
• Other 
IDCountry (Location)WWTPs or SPsStudy TypeStudy PeriodVariant Detection MethodGenes Tested for Variant DetectionNGS PlatformaNGS PrimersVariant ReportingEarly Warning WindowVariantsb
Agrawal et al. (2022a)  Pan-European (20 Countries) 54 WWTPs Population Level 2021/03
2021/03 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Agrawal et al. (2022b)  DE (Frankfurt) 1 WWTP,
1 SP 
Population Level,
Hotspot (Airport) 
2021/11
2021/11 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Detection • BA 
Ahmed et al. (2022b)  AU (Eaton) 12 SP Hotspot (Airport) 2021/04
2021/08 
Targeted NGS
RT-PCR 
Nanopore, Atoplex ARTIC V3
ATOPlex SARS-CoV-2 full-length genome panel 
Proportion • BA 
Ai et al. (2021)  US (Ohio) 9 WWTPs Population Level 2020/07
2021/01 
Targeted NGS – Illumina CovidSeq Proportion • B.1.1.7
• Other 
Amman et al. (2022)  AT (National) 94 WWTPs Population Level 2020/12
2022/02 
Targeted NGS – Illumina ARTIC VarSkip 1a Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
Avgeris et al. (2021)  GR (Athens) 1 WWTP Population Level 2020/09
2020/11 
Targeted NGS S
3a
1ab 
Ion Torrent In-house Primers Proportion – • B.1.1.7 
Bagutti et al. (2022)  CH (Basel) 1 WWTP Population Level 2021/07
2021/12 
Targeted NGS – Illumina ARTIC V4 Proportion • B.1.617.2
• BA 
Bar-Or et al. (2022)  IL (National) 13 WWTP Population Level 2020/12
2021/03 
Targeted NGS  Illumina Illumina COVID-seq kit. ARTIC V4 Proportion – • B.1.1.7 
Bar-Or et al. (2021)  IL (National) 9 WWTPs Population Level 2020/08
2021/02 
Targeted NGS – Illumina ARTIC Proportion • B.1.1.7
• B.1.351
• P.1
• Other 
Boehm et al. (2022)  US (California) 8 WWTPs Population Level 2022/01
2022/04 
ddRT-PCR N
– – Proportion 10.5 • BA 
Brumfield et al. (2022)  US (Maryland) 1 SP Hotspot (Neighbourhood) 2020/12
2021/11 
WGS;
Targeted NGS 
– Illumina Swift Normalase Amplicon SARS-CoV-2 Panel kit Proportion – • B.1.1.7
• B.1.351 
Caduff et al. (2022)  CH (Zurich) 1 WWTP Population Level 2020/12
2022/03 
RT-ddPCR S
ORF1a 
– – Proportion 7.3 • B.1.1.7
• B.1.351
• P.1 
Carcereny et al. (2022)  ES (Catalonia) 14 WWTP Population Level 2020/11
2021/04 
RT-qPCR;
Targeted NGS 
Illumina ARTIC V3 Proportion – • B.1.1.7 
Carcereny et al. (2021)  ES (National) 32 WWTPs Population Level 2020/12
2021/03 
RT-qPCR;
Targeted NGS 
Illumina ARTIC V3 Proportion – • B.1.1.7 
Chassalevris et al. (2022)  GR (Thessaloniki) 1 WWTP Population Level 2021/11
2022/01 
RT-qPCR – – Proportion • BA 
Cutrupi et al. (2022)  IT (Trento) 2 WWTPs Population Level 2020/12
2022/04 
RT-qPCR;
Sanger sequencing;
Targeted NGS 
Nanopore – Detection • B.1.617.2
• BA 
Dharmadhikari et al. (2022)  IN (Pune) 2 WWTPs Population Level 2020/12
2021/03 
Targeted NGS – Nanopore nCoV-2019 sequencing protocol v3 (LoCost) V3 Detection NR • B.1.617.2 
El-Malah et al. (2022)  QA (National) 5 WWTPs Population Level 2021/03
2021/04 
Targeted NGS – Illumina CleanPlex SARS-CoV-2 Panel Proportion – • B.1.1.7
• B.1.351
• B.1.617.2 
Graber et al. (2021)  CA (Ottawa) 2 WWTPs Population Level 2021/01
2021/04 
RT-qPCR – – Proportion – • B.1.1.7 
Heijnen et al. (2021)  NL (Amsterdam, Utrecht) 2 WWTPs Population Level 2020/11
2021/03 
RT-ddPCR – – Proportion – • B.1.1.7
• B.1.351 
Herold et al. (2021)  LU (Luxembourg) 13 WWTPs Population Level 2020/03
2021/03 
Targeted NGS – Illumina ARTIC V1 Proportion – • B.1.1.7
• B.1.351
• P.1 
Ho et al. (2022)  DE (Karlsruhe) 1 WWTP Population Level 2020/06
2021/07 
RT-ddPCR S (N501Y) – – Proportion – • B.1.1.7 
Hubert et al. (2022)  CA (Alberta) 24 WWTPs Population Level 2021/11
2022/01 
RT-qPCR – – Proportion • B.1.617.2
• BA 
Jahn et al. (2022)  CH (National) 2 WWTPs,
1 SP 
Population Level,
Hotspot (Ski Resort) 
2020/07
2021/09 
Targeted NGS – Illumina ARTIC v3/v4 Proportion 65.5 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Johnson et al. (2022)  ZA (Cape Town) 24 WWTPs Population Level 2021/05
2021/07 
RT-qPCR, Targeted NGS DNBSEQ ATOPlex for SARS-CoV-2 fragments Proportion – • B.1.1.7
• B.1.351
• B.1.617.2 
Joshi et al. (2022)  IN (Ahmedabad) 1 WWTP Population Level 2020/11
2021/02 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 Research Panel Detection 30 • B.1.617.2 
Karthikeyan et al. (2022)  US
(San Diego) 
1 WWTP,
131 SPs 
Population Level,
Hotspot (Campus) 
2020/11
2021/09 
Targeted NGS
RT-qPCR 
c Illumina COVG1V2-96 Proportion 14 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
La Rosa et al. (2021)  IT (Latina Province) 3 WWTPs Population Level 2021/04
2021/05 
RT-nested-PCR, WGS Nanopore – Proportion – • B.1.1.7
• P.1
• Other 
La Rosa et al. (2022)  IT (National) 134 WWTPs Population Level 2021/11
2021/12 
RT-qPCR and Sanger sequencing – – Proportion – • BA 
Layton et al. (2022)  US (Oregon city) 6 WWTPs,
22 SPs 
Population Level 2020/04
2021/05 
Targeted NGS – Illumina Swift Amplicon SARS-CoV-2 Panel Proportion – • NR 
Lee et al. (2022)  IT (Brescia) 1 WWTP Population Level 2021/04
2022/01 
RT-qPCR – – Proportion – • B.1.617.2
• BA 
Lee et al. (2021)  US (11 US States) 16 WWTPs Population Level 2020/10
2021/03 
RT-qPCR – – Proportion – • B.1.1.7 
Li et al. (2022c)  US (Nevada) 3 WWTPs Population Level 2020/11
2021/06 
Targeted NGS – Illumina Mybait probe enrichment Proportion – • B.1.1.7
• P.1
• B.1.617.2
• Other 
Markt et al. (2022)  LI (National) 1 WWTP Population Level 2020/09
2021/03 
Targeted NGS – Illumina ARTIC V3 Proportion 21 • B.1.1.7 
Masachessi et al. (2022)  AR
(Córdoba) 
4 WWTPs Population Level 2020/05
2021/08 
RT-qPCR – – Detection – • B.1.1.7
• P.1
• B.1.617.2 
Nag et al. (2022)  IN (Jaipur) 11 WWTPs Population Level 2021/02
2021/06 
Targeted NGS – Illumina NGSeq ARTIC Sars-CoV-2 kit Detection – • B.1.617.2 
Novoa et al. (2022)  ES (Galicia) 11 WWTPs Population Level 2020/05
2021/05 
Targeted NGS – Illumina ARTIC V3 Proportion – • B.1.1.7
• B.1.351
• P.1
• Other 
Oloye et al. (2022)  CA (Prince Albert)  3 WWTPs Population Level 2021/08
2022/01 
RT-qPCR Targeted NGS S
Illumina ARTIC V3 Proportion – • B.1.617.2
• BA 
Pechlivanis et al. (2022)  GR (Thessaloniki) 1 WWTP Population Level 2020/12
2021/04 
Targeted NGS – Illumina ARTIC V3 Proportion – • B.1.1.7
• B.1.351 
Perez-Cataluna et al. (2022)  ES (National) 14 WWTPs Population Level 2020/04
2021/01 
Targeted NGS – Illumina ARTIC V3 Proportion 42 • B.1.1.7 
Radu et al. (2022)  AT (Vienna) 1 WWTP Population Level 2020/10
2021/05 
RT-qPCR Targeted NGS Illumina ARTIC V3 Proportion 14 • B.1.1.7
• P.1 
Reynolds et al. (2022)  IE (Dublin) 1 WWTP Population Level 2020/06
2021/08 
RT-ddPCR – – Proportion 21 • B.1.1.7
• B.1.617.2 
Rios et al. (2021)  FR (Nice) 1 WWTPs,
20 SPs 
Population Level 2020/10
2021/03 
Targeted NGS – Nanopore ARTIC v3 Proportion – • B.1.1.7
• B.1.351
• P.1
• Other 
Roka et al. (2022)  HU (National) 22 WWTPs Population Level 2020/12
2021/03 
RT-ddPCR Melting Curve Genotyping – – Proportion – • B.1.1.7 
Rouchka et al. (2021)  US (Louisville) 5 WWTPs Population Level 2020/10
2021/01 
Targeted NGS – Illumina Swift Normalase Amplicon Panel (SNAP) SARS-CoV-2 Proportion 21 • B.1.1.7
• B.1.351
• P.1
• Other 
Smyth et al. (2022)  US (New York City) 14 WWTPs Population Level 2021/01
2021/06 
Targeted NGS – Illumina S gene specific primers Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• Other 
Sutton et al. (2022)  US (Oregon) 3 WWTPs Population Level 2021/03
2021/04 
Targeted NGS – Illumina Swift Amplicon SARS-CoV-2 Panel Proportion 12 • B.1.351 
Swift et al. (2021)  US (South Carolina) 2 WWTPs Population Level 2020/07
2021/01 
Targeted NGS – Nanopore ARTIC V3 Detection – • B.1.351
• P.1
• B.1.617.2
• Other 
Vo et al. (2022)  US (Las Vegas) 7 WWTPs Population Level 2020/03
2021/04 
Targeted NGS – Illumina CleanPlex SARS-CoV-2 FLEX Panel Proportion 30 • B.1.1.7
• Other 
Wang et al. (2022b)  AU (Queensland) 18 WWTPs,
1 SP 
Population Level, Hotspot (Aircraft) 2021/08
2021/09 
Targeted NGS – DNBSEQ ATOPlex Detection – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
RT-qPCR – – Detection – • BA 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
RT-dPCR N
– – Proportion  • BA 
Wilhelm et al. (2022) d DE (Dinslaken) 1 WWTP Population Level 2021/12
2022/01 
Targeted NGS – Ion Torrent Ion AmpliSeq SARS-CoV-2 research panel Detection – • BA 
Wolfe et al. (2022)  US (California) 1 WWTP Population Level 2020/08
2022/01 
RT-ddPCR S
ORF3a 
– – Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• BA
• Other 
Wurtz et al. (2021) e FR (Marseille) 2 WWTPs Population Level 2021/04
2021/04 
RT-qPCR – – Proportion – • B.1.1.7 
Wurtz et al. (2021) e FR (Marseille) 2 WWTPs Population Level 2021/04
2021/04 
Targeted NGS – Illumina TIC nCoV-2019 V3 Panel (LoCost ARTIC) Proportion – • B.1.1.7
• B.1.351
• P.1
• B.1.617.2
• Other 
Wurtzer et al. (2022)  FR (Paris) 5 WWTPs Population Level 2020/03
2021/06 
RT-qPCR – – Proportion • B.1.1.7 
Xie et al. (2022)  CA (Saskatoon City) 1 WWTP Population Level 2020/07
2021/08 
RT-ddPCR N
– – Proportion 10.5 • B.1.1.7
• B.1.351
• P.1
• B.1.617.2 
Xu et al. (2022)  HK (Hong Kong) 1 SPs Hotspot (Hotel) NR WGS
Targeted NGS 
Nanopore ARTIC V3 Detection • B.1.351
• B.1.617.2
• BA 
Yaniv et al. (2021)  IL (2 cities) 1 WWTP,
12 SPs 
Population Level 2020/11
2021/10 
RT-qPCR – – Detection – • B.1.1.7
• B.1.617.2 
Yaniv et al. (2022)  IL (Beer-Sheva) 1 WWTP Population Level 2021/12
2022/01 
RT-qPCR – – Proportion • B.1.617.2
• BA 
Yu et al. (2022)  US (California) 2 SPs Population Level 2020/07
2021/08 
RT-ddPCR – – Proportion 14 • B.1.1.7
• B.1.617.2 
Rubio-Acero et al. (2021)  DE (Munich) 6 WWTPs Population Level 2020/04
2021/04 
Targeted NGS – Illumina ARTIC Proportion 14 • B.1.1.7
• B.1.351
• P.1 
Tandukar et al. (2022)  NP (Kathmandu Valley) 2 WWTPs, 1 SP Population Level
Hotspot (hospital) 
2020/07
2021/02 
qRT-PCR N
– – Detection – • B.1.1.7 
Galani et al. (2022)  GR (Attica) 1 WWTP Population Level 2020/08
2021/03 
Targeted NGS – Ion Torrent – Proportion • B.1.1.7 
Deng et al. (2022a)  HK (Hong Kong) 112 SPs Hotspot (Neighbourhood) 2020/12
2021/06 
RT-qPCR
Targeted NGS 
Illumina ARTIC Detection • B.1.617.2 
Corchis-Scott et al. (2021)  CA (Windsor) 1 WWTPs Hotspot (Campus) 2021/02
2021/03 
RT-qPCR – – Detection • B.1.1.7
• Other 
de Llanos et al. (2022)  ES (Castellon) 12 SPs Hotspot (Campus) 2020/10
2021/07 
Targeted NGS – Illumina ARTIC Detection – • B.1.617.2
• Other 

a• Other refers to any VOC not B.1.1.7, B.1.617.2, B.1.351, P.1, and BA. Full list of VOCs detected in each manuscript can be found in Supplementary Information.

bRT-qPCR – Quantitative reverse transcription polymerase chain reaction; RT-dPCR – Reverse-transcription digital PCR; RT-ddPCR – Droplet Digital reverse transcription polymerase chain reaction.

cSee the Supplementary Information for full list of mutations.

d,e = studies with different analysis pipelines.

NR = not reported.

The studies assessing SARS-CoV-2 variant detection were conducted between March 2020 and April 2022 with a median duration of six months. Most of the publications (91%) focused on the presence of variants at the population level by sampling the influent at WWTPs or sewers. Furthermore, some studies performed the analysis at hotspots, including three at campus residences (Corchis-Scott et al. 2021; de Llanos et al. 2022; Karthikeyan et al. 2022), two at airports (Agrawal et al. 2022b; Ahmed et al. 2022b), two from neighborhoods (Brumfield et al. 2022; Deng et al. 2022a), one from an aircraft (Wang et al. 2022b), one from a hotel (Xu et al. 2022), and one from a ski resort (Jahn et al. 2022). In terms of timeliness, 23 manuscripts (35%) reported EWS for variants with a median lead time of 11.2 days [IQR 4.5–19.2] compared to clinical surveillance.

Several molecular methods were used for variant assignment, but 46% (n = 30) of the studies used targeted-NGS and 29% (n = 19) used RT-PCR (i.e., RT-qPCR, RT-dPCR, RT-ddPCR). Other studies used a combination of targeted-NGS and RT-PCR (n = 10, 15%), and two used WGS and RT-PCR (3%). One study used WGS combined with targeted NGS, one used RT-PCR combined with Sanger sequencing, another used targeted-NGS, RT-PCR and Sanger sequencing, and one study used RT-qPCR with melting curve analysis. Among the different sequencing technologies, Illumina-sequencing was employed most frequently (n = 28, 65%), followed by Nanopore (n = 7, 16%), Ion Torrent (n = 6, 14%), and DNBSEQ (n = 2, 5%). Most of the methods relied on reference mapping for variant assignment. Summary information on variant assignment is not feasible due to heterogeneity in methods and unclear method-reporting in the manuscripts. Additional information on NGS instruments, primers used for amplicon sequencing, and different software used in variant assignment can be found in Supplementary information (Section 5.3). RT-PCR was used in 33 instances for variant assignment. The S gene was the most common target (n = 22, 67%), followed by a combination of N and S genes (n = 5, 15%), and N (n = 3, 9%).

Any variant assigned as VOCs by the World Health Organization (WHO) or ECDC was recorded and is available in the Supplementary information (section 4). All non-VOC variants were recorded as ‘others’. The median number of VOCs reported per study was two. As expected, studies using NGS had a larger range of possible VOCs than those using RT-PCR (1–14 and 1–7, respectively). Most of the studies reported relative proportions of variants in wastewater (n = 51, 78%) and the remaining 14 only reported variant detection (22%). The B.1.1.7 variant was the variant most frequently reported (n = 45, 69%), followed by B.1.617.2 (n = 29, 45%), B.1.351 (n = 24, 37%), P.1 (n = 21, 32%), and BA (including BA.1 or BA.2, also known as B.1.1.529.1 and B.1.1.529.2, respectively; n = 17, 26%) (Figure 2). One study did not identify any VOCs (Layton et al. 2022).
Figure 2

Variants of concern included in each study (left) represented in red (B.1.351), blue (B.1.1.7), green (P.1), purple (B.1.617.2), yellow (BA), and grey (Other). The ES study duration is represented with horizontal solid black lines (right). Vertical solid-colored lines indicate the earliest detection of a variant and the dotted lines the date of designation as variants of concern. Superscript ‘a’ indicates studies that did not report sampling period.

Figure 2

Variants of concern included in each study (left) represented in red (B.1.351), blue (B.1.1.7), green (P.1), purple (B.1.617.2), yellow (BA), and grey (Other). The ES study duration is represented with horizontal solid black lines (right). Vertical solid-colored lines indicate the earliest detection of a variant and the dotted lines the date of designation as variants of concern. Superscript ‘a’ indicates studies that did not report sampling period.

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Public health impact and control measures

Seven of the publications included for the evaluation of the effectiveness of ES as an EWS explored the potential of ES to inform public health response and control measures during the different phases of the pandemic. At the campus level, Corchis-Scott et al. (2021) triggered a public health case-finding response the day after a positive detection in wastewater, while Karthikeyan et al. (2021) developed a notification system to alert residents after positive detection encouraging testing at no charge. Kotay et al. (2022) performed building-level sewage surveillance to proactively identify hotspots and inform decision-making actors to trigger the point of prevalence in individual dormitories. Lu et al. (2022) used wastewater monitoring data to perform focused surveillance of specific dormitories or possible hotspots while remaining more cost-effective and less intrusive. Finally, Reeves et al. (2021) performed daily reporting of the wastewater surveillance findings to assist in decision-making during critical phases of the pandemic on campus. Deng et al. (2022a, 2022b) performed two studies in Hong Kong where sewage surveillance was used to guide public health interventions by developing proactive responses to COVID-19 and issuing compulsory testing in areas with positive detection.

Furthermore, a grey literature search for assessing information on public health impact resulted in 30 publications. Among these, there were 13 reports, eight guidelines, two articles, two policy briefs, two scientific briefs, two webpages, and one Q&A (Supplementary information, Table S1). Some countries such as Canada, Denmark, Spain, and Italy published reports on the evaluation of ES for SARS-CoV-2 compared to clinical data (H.M. Spain 2021; S. S. Institut 2021; ISS 2022; Ontario 2022). There seems to be a consensus within the four countries on the unique benefits of this approach as a complementary and high valuable tool for obtaining information on disease outbreaks, particularly for monitoring population-level trends. However, the uncertainty factors affecting the SARS-CoV-2 estimates from wastewater analysis such as different shedding rates, sampling approaches, population dynamics or testing methodologies, have a big impact on the interpretation of the results. Therefore, more understanding about the context of the data is needed before public health actions can be implemented at the national level (Ontario 2022).

Other factors related to public health impact, such as cost, have also been addressed. Estimated start-up and operating costs vary considerably depending on calculation methodology, and actual costs also differ across programs. The World Bank estimates a cost of US$0.50 per person per year for wastewater testing for a population of 100,000 twice per week, not including the cost of sampling, transportation, and data interpretation (Manuel et al. 2022). In their report, the estimated cost of clinical testing for the Latin American and Caribbean population was estimated to be approximately US$5.80 per person per year, more than 10 times the estimated cost of establishing wastewater surveillance in the first year (Manuel et al. 2022). The European Joint Research Centre published the results of a survey conducted in eight European countries with a cost estimate of €25,000 for the analysis of two wastewater samples per week during a year, a cost of €0.25 per person for sites with 100,000 people including sampling, shipment and laboratory tests using their own instrumentation (Gawlik et al. 2021). Literature related to ethical guidelines for wastewater surveillance for SARS-CoV-2 has been limited, despite calls for the development of such guidance (Hrudey et al. 2022).

Risk of bias – outcome of ROBINS-I assessment and cumulative body of evidence

The overall risk of bias in the included studies was assessed to be moderate according to the ROBINS-I tool, although not all domains of bias in the tool were applicable due to the heterogeneity of the included studies (Supplementary information). The cumulative body of evidence was graded according to intervention studies in the domain of publication bias according to the PRECEPT framework as having a moderate risk of publication bias, although the PRECEPT framework could only be partly applied for the same reasons as mentioned above.

This systematic review provides an update of the previously published review focused on the effectiveness of the ES of SARS-CoV-2 as an EWS, the added value of ES as a public health tool to monitor SARS-CoV-2 in different targeted areas as well as its ability to detect new variants (Hyllestad et al. 2022).

This review updating the second pandemic year includes 151 articles published between June 2021 and July 2022, as compared to 35 in our first systematic review, highlighting the rapid developments on this topic from the international scientific community where North America accounts for the highest number of publications, followed by Europe.

The included publications are heterogeneous in terms of sampling locations, sampling type, mode, frequency, target genes used to detect SARS-CoV-2 in wastewater, analytical methods, and data analysis, reflecting a lack of methods' harmonization in different countries. These factors made it challenging to compare and evaluate the outcomes of each study to support the aim of this systematic review with a meta-analysis (Gough et al. 2017). However, it was possible to provide a narrative summary of the findings covering all the study's objectives such as timeliness, sensitivity, specificity, early detection of variants, public health impact and usefulness.

Timeliness: Early detection of waves of infections

The study period for the 92 articles included for the assessment of timeliness had a length of approximately six months. During this period, most of the articles (n = 47, 51%) supported the potential of ES as an EWS for SARS-CoV-2 infections with approximately 1–2 weeks of lead time. This goes in line with the findings from our previous systematic review by Hyllestad et al. (2022) 

However, the evidence presented to support these results is not harmonized between the different studies. For instance, although 98% of the studies compared wastewater-based data with a confirmed number of daily cases, the approaches used were different from each other and not comparable (i.e., plotting clinical and wastewater data without any statistical approach (n = 13), correlation analysis (n = 35) or modeling (n = 17). Some of these approaches are very promising but optimized for specific settings and not suitable for different ES systems: (i) a Monte-Carlo simulated fecal load and shedding rate per person in combination with SARS-CoV-2 wastewater concentration to assess the correlation with clinical data (Claro et al. 2021; de Sousa et al. 2022), (ii) a model for ES data to estimate the effective reproduction number Re from wastewater data (Huisman et al. 2022), (iii) an extended version of an SEIR (‘Susceptible – Exposed – Infectious – Recovered’) model to make forecasts with varying success (Nourbakhsh et al. 2022), (iv) a Generalized Additive Model (GAM) trained with registered case numbers to predict prevalence numbers from SARS-CoV-2 concentrations and flow data (Vallejo et al. 2022), or (v) an artificial neural network (ANN) trained with wastewater data and other parameters to predict case data (Jiang et al. 2022).

Furthermore, most of the publications presented retrospective analysis and did not state the exact date when both wastewater and clinical datasets were available for public health action, which is critical for the assessment of timeliness. Better reporting on data availability is needed to evaluate the effectiveness of this approach. This factor, and the use of different national infrastructure, surveillance of sampling sites with different logistic challenges, lack of harmonized molecular detection methods and varying clinical testing capacity within and between countries, makes the assessment of timeliness challenging.

Sensitivity and specificity

ES estimates of the sensitivity of SARS-CoV-2 viral detection in wastewater available in the literature have varied widely (Hewitt et al. 2022). These differences may be caused by different factors, such as the lack of reliable prevalence and incidence data, variation in the COVID-19 testing rates in the study area or population mobility dynamics. Furthermore, large variations in the viral concentration per gram of feces between different infected individuals over the course of a single or new infection wave will result in large variability in the concentrations found in wastewater, impacting the estimation of the sensitivity (Cavany et al. 2022; Li et al. 2022a).

The variation in the number of asymptomatic and pre-symptomatic individuals affecting the measured concentrations of SARS-CoV-2 in wastewater is another big challenge when comparing ES data with data based on symptomatic individuals (Deng et al. 2022b). Some of the publications included in this systematic review confronted this challenge by performing their work under controlled settings facilitating the detection of new clinical cases, including asymptomatic ones (Deng et al. 2022b; Godinez et al. 2022; Hewitt et al. 2022; Wang et al. 2022a; Welling et al. 2022). Although the level of uncertainty (based on the different study settings) reporting ES's sensitivity levels varied widely, the low estimated incidence rate detection limit of this approach reported in some of the publications increases optimism for the future application of ES (Wolfe et al. 2021; Hewitt et al. 2022; Li et al. 2023).

These results support the potential use of ES as a sensitive tool under controlled settings, particularly to provide EW signals of undetected cases. Furthermore, ES could be most useful as EWS in low-prevalence situations, in situations and areas where there is limited capacity for individual testing, or in situations where there is a shift from testing notified in national health systems to private at-home testing (Cavany et al. 2022). However, the number of studies reporting on this subject is low (n = 8). Therefore, more research development for the assessment of ES's sensitivity and specificity needs to be considered in the future.

Factors influencing effectiveness of ES

ES has proven effective in monitoring the SARS-CoV-2 virus at various scales, leading to international initiatives and delivering valuable insights for public health decision-making during the pandemic (Lundy et al. 2021; Hrudey & Conant 2022). However, there are many factors that can negatively impact the effectiveness of ES.

An important challenge influencing the effectiveness of ES for public health decision-making is the collection and transportation of wastewater samples to the laboratory. This issue directly affects the timeliness and efficiency of ES as a tool for rapid intervention and informing public health decisions, ultimately hindering its scalability and sustainability (E.a.C.C. Canada 2021; CDC 2022a; World Health Organization 2022a). Of the publications included in this systematic review for the assessment of the effectiveness, 80% (n = 92) used the collection of wastewater composite samples for better representativeness, improving the 68% of the previous review (Hyllestad et al. 2022). Furthermore, 87% of the studies in the present revision kept the wastewater samples at 4 °C prior to analysis, in line with stability test studies (Tavazzi et al. 2023). Few studies reported the time between sample collection, analysis completion and data availability. The development of innovative in-field automated tools to enhance the speed at which data are provided to inform public health policymakers will be key in the future (Singer et al. 2023).

The level of representativeness of the target being monitored is another critical factor for the effectiveness of ES since it affects the overall sensitivity and specificity of the approach (Wade et al. 2022; World Health Organization 2022a). There are several reports extracted from the grey literature on best practice guidelines for ES (E.a.C.C. Canada 2021; CDC 2022a, 2022b; Ontario 2022; The Association of Public Health Laboratories A. 2022). The recommended approaches from these reports will help consolidate research efforts improving the current lack of harmonization in sampling procedures, analytical methods, data processing and interpretation. However, heterogeneity and differences in the public health reporting systems, spatial misalignment between administrative areas and sewer catchment areas or differences in the sewer network systems themselves need to be considered. Therefore, a high degree of harmonization among different sewer and public health systems in different countries may not be realistic.

The 90% of the publications included for the assessment of the effectiveness (n = 92) used RT-qPCR as an analytical technique. However, these studies used different virus concentration methods, different target genes and different normalization approaches. No comparison approaches (i.e., inter-laboratory) were reported. Furthermore, a striking 28% of the publications did not normalize the data against wastewater dilution factors or population dynamics.

Assessing the uncertainty related to multiple factors interfering in the variability of the measured signal from the wastewater sample is one of the key challenges to securing the sustainability of this approach for public health actions. However, the current findings suggest that ES approaches are still far from standardized globally (Wade et al. 2022).

Early detection of variants

The assessment of early detection of variants based on 65 publications indicates that the identification of variants in wastewater is feasible and can provide timely information. Among these studies, NGS was the most frequently used approach to investigate variants in wastewater. This suggests that despite the higher cost of NGS and the increased technical skills necessary to process this data, these methodologies are being preferred over qPCR due to the added value of i) identification of all possible variants, ii) proportion of variants, and iii) availability of data for further investigation (Itarte et al. 2021). Furthermore, NGS provides a better measure to identify shifts in variant populations over time especially when sequences of potential new VOCs are unknown. However, comparing the output from NGS across studies is difficult given the diverse use of sequencing technologies and downstream analysis pipelines (i.e., from raw data to variant assignment) (Tamáš et al. 2022).

Despite the advantages of NGS, qPCR is a fast and inexpensive alternative particularly suitable for screening purposes. This could be useful when a given VOC has not been detected in clinical surveillance, but it is known to be circulating in other geographical areas. The differences between NGS and qPCR indicate that both methodologies can be important in the surveillance of SARS-CoV-2 in wastewater, and both can be performed individually or in combination to address specific surveillance needs. For both qPCR and NGS, a variant assignment can be affected by the sample preparation, inhibition, or degradation (Ahmed et al. 2022a). This can be particularly problematic for NGS data when genome coverage is insufficient to perform variant assignment. Future efforts should envision the creation of bioinformatic best practices in variant assignment from complex biological samples so that the output from EWS can be compared across different geo-temporal scales and provide accurate data for public health actions.

The retrospective nature of the manuscripts also highlights a drawback in the interpretation of the EWS identified in one-third of the publications. Since most manuscripts will be published after many variants are classified as VOCs, EWS is calculated based on the comparison with clinical data and not in real-time. Considering the potential for delays in sequencing and data analysis, a proper assessment of variant EWS would need a different study design. Any expectation of meaningful EWS will require sufficient sampling frequency combined with rapid sample processing, analysis, reporting and potential public health action (Manuel et al. 2021; Safford et al. 2022; World Health Organization 2022a).

Overall, the information in this updated systematic review reinforces the assessments made in the previous systematic review in terms of the important role of ES as complementary to clinical surveillance and its research perspective (Hyllestad et al. 2022). ES could function as an EWS in given geographic areas where VOCs are still not reported (Ahmed et al. 2022b). Yet, inferring variant-specific transmission rates is still difficult and necessitates the knowledge of other parameters (e.g., variant-specific shedding loads) which will not be available when a new variant emerges (C.f.D. Control & Prevention 2022). Therefore, clinical surveillance is also critically important for ES to understand the impact of emerging variants (i.e., disease severity, hospitalizations), supporting the complementarity of these two surveillance systems.

Public health usefulness

The WHO together with a group of international specialists assessed the public health usefulness of ES for decision-making (World Health Organization 2022a). Overall, we found a consensus on the main uses of ES for public health (C.f.D. Control & Prevention 2022; Hrudey et al. 2022; Manuel et al. 2022):

  • Early detection of outbreaks: in places thought to be free of COVID-19.

  • Population-wide surveillance: tracking increasing and decreasing pandemic trends.

  • Specific population surveillance: surveillance of vulnerable or high-risk settings.

  • Variants of concern: detecting the emergence of novel variants.

However, there are some barriers to accomplishing these use cases such as (i) the lack of evaluation guidelines for the ES programs, (ii) lack of optimal communication and collaboration between agencies, (iii) lack of institutional knowledge and/or resources, or (iv) ethical considerations.

The evaluation protocol for the usefulness of ES programs should be based on the benefits for public health and end-users, led by specific public health measures or actions triggered using wastewater-based data. Yet, few of the published articles or reports are assessing actual public health response and control measures triggered by ES (Corchis-Scott et al. 2021; Karthikeyan et al. 2021; McClary-Gutierrez et al. 2021b; Reeves et al. 2021; Deng et al. 2022a, 2022b; Kotay et al. 2022; Lu et al. 2022).

Understanding how the information can be used is essential to evaluate the ultimate benefits and limitations of this approach. A clearly defined and rapid response strategy and chain of communication across public health practitioners, healthcare, policymakers, researchers, environmental experts, and law enforcement is an important factor influencing the effectiveness of ES (Keshaviah et al. 2022; Mohan et al. 2022; Ontario 2022). In this systematic review, 27% of the included publications screened for the effectiveness of ES as EWS included authors from at least three out of the four different scientific areas selected, while 21% of the publications had authors from only one of the sectors. Yet, most of the publications originated from the research environment rather than established public health surveillance systems, in line with the observations found in the first review.

In monetary terms, there seems to be agreement on the cost-effectiveness of ES of SARS-CoV-2 compared to other surveillance systems (Manuel et al. 2021, 2022; Hrudey et al. 2022; Safford et al. 2022; World Health Organization 2022a). However, reports claiming the cost-effectiveness of ES did not present any data from both the wastewater and clinical surveillance systems. This is mainly due to the wide variation of the start-up and operating costs for each of the surveillance systems at the national level. Yet, the World Bank estimates that clinical testing for the Latin American and Caribbean populations was approximately 10 times more expensive than ES (World Health Organization 2022b). Therefore, ad hoc studies are needed to evaluate the cost-effectiveness of ES considering different phases of the pandemic, countries' testing capacities and available resources.

In addition, efforts must be made to evaluate and establish best practices, so as to ensure environmental and social justice, protect privacy, follow applicable law, prevent improper use of data, and avoid adverse community and individual outcomes. These efforts involve engaging the public, developing legal frameworks, and conducting research and data collection to ensure ethical and equitable wastewater surveillance programs to support public health response. This might be relatively easy for ES at the population level, but challenging in the context of future near-to-source ES that can be directly tied to a small number of individuals in a known location ((EPA) U.S.E.P.A. 2021; McClary-Gutierrez et al. 2021b; Gutierrez et al. 2022; Hrudey et al. 2022).

Limitations of the review

The assessment of performance attributes (i.e., sensitivity, specificity, timeliness, public health impact) will depend on the context and objective of the given wastewater surveillance system. Since this will likely vary across the reviewed publications, care needs to be taken when making conclusions about the effectiveness of ES in general. The number of studies on ES to monitor SARS-CoV-2 has increased. However, due to the heterogeneity of the included publications, a proper assessment of the risk of bias is challenging.

Following the ROBINS-I tool, mimicking a hypothetical RCT, and applying PRECEPT for grading the cumulative body of evidence is challenging, which affects the strength of the outcome in terms of evidence of the effectiveness of ES as an EWS for SARS-CoV-2 of the review (Sterne et al. 2016; Harder et al. 2017).

By analyzing a total of 151 articles published between June 2021 and August 2022, we have assessed valuable insights into the timeliness, sensitivity, specificity, early detection of variants, and public health impact of ES. These results confirm the potential of ES as an EWS for detecting waves of infection approximately 1–2 weeks earlier than clinical data, in line with our previous systematic review. However, the majority of these claims were based on retrospective analysis, not as a real-time EWS.

While the usefulness of wastewater surveillance has been recognized, some of the factors influencing the effectiveness of ES such as lack of standardized protocols and data harmonization, enhanced integration with clinical surveillance for public health response strategies, and evaluation studies confirming its impact on specific public health measures or actions, are still lacking in this update of the second year of the pandemic, in line with the first review. Furthermore, reporting data confirming the claims on the cost-effectiveness of ES is not included in most publications.

Data suggest that ES is sensitive in detecting new waves of infections, particularly in settings with low prevalence. However, the presence of asymptomatic and convalescent individuals poses challenges in interpreting wastewater data in real-time. Strategies to address this challenge include exploring alternative sampling methods, testing approaches, and modeling techniques.

While in the first review we found low evidence for the added value of ES as EWS for new VOCs, in this update review, data show that ES can complement clinical surveillance and function as an EWS in areas where the designated VOC is not reported or there is limited clinical surveillance capacity. The timely identification of the new VOCs through ES plays an important role in preparedness plans for future outbreaks allowing for the implementation of early action measures such as adaptive resource allocation, population mobility restrictions or vaccine distribution. The use of NGS technologies has facilitated the identification and monitoring of novel variants. However, establishing robust methodologies for variant detection and the creation of bioinformatic best practices for variant assignment are needed to better compare the ES results from different studies and locations.

Innovations in automated tools to enhance the speed and efficiency of data delivery, optimized strategies to assess uncertainty levels for data representativeness' and strategies to integrate and utilize wastewater data to support evidence-based decision-making are some of the future priorities to enhance the effectiveness of this approach. Furthermore, the allocation of sufficient institutional knowledge and resources is crucial for evaluating and implementing wastewater-based data into disease surveillance programs. Cross-sectoral collaboration among stakeholders and investment in technological advancements will be crucial for maximizing the effectiveness of wastewater surveillance and harnessing its benefits for public health decision-making.

We thank research librarian Bente Foss at the Norwegian Institute of Public Health for developing and conducting the literature search. João Pires is financially supported by the European Programme for Public Health Microbiology Training (EUPHEM), ECDC. The funder had no role in study design, data collection and interpretation, or the decision to submit the work.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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