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Time scale of clinical data collection and wastewater surveillance (incubation time and shedding duration are summarized in Tables S1 and S2, respectively).
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Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 20...
Contexts in source publication
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... of the factors contributing to the lag time between wastewaterbased data peaks and clinical data peaks are visualized in the timeline shown in Fig. 1. The majority of published studies show empirical evidence that the range of the incubation time of SARS-CoV-2 prior to the Omicron surge was 0 to 14 days (shown in Table S1 and summarized in Fig. 1). Clinical testing is often delayed from the manifestation of clinical symptoms by several days, due to limited availability of testing ...
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... of the factors contributing to the lag time between wastewaterbased data peaks and clinical data peaks are visualized in the timeline shown in Fig. 1. The majority of published studies show empirical evidence that the range of the incubation time of SARS-CoV-2 prior to the Omicron surge was 0 to 14 days (shown in Table S1 and summarized in Fig. 1). Clinical testing is often delayed from the manifestation of clinical symptoms by several days, due to limited availability of testing supplies, limited ability to reach testing sites, or resistance of people to seek testing (Rader, 2020;Torres et al., 2021;Wiens et al., 2021). In some cases, the mean delay in the reporting of ...
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... particularly in demographically and socioeconomically varied populations, like that of Detroit, Michigan. Generally, between day 19 and 25, clinical data will become publicly available, after an estimated delay of 3 to 9 days of clinical data collection and processing time ( Garg et al., 2020;Harris, 2020;Rader, 2020). Additionally, Fig. 1 demonstrates the temporal progress of data collection of viral loadings in wastewater. The detention time of wastewater in the collection network is estimated as 12 to 24 h (Table S4). In Fig. 1 it is assumed that in most cases, wastewater laboratory tests are completed within a day upon sample collection. A compilation of all the ...
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... after an estimated delay of 3 to 9 days of clinical data collection and processing time ( Garg et al., 2020;Harris, 2020;Rader, 2020). Additionally, Fig. 1 demonstrates the temporal progress of data collection of viral loadings in wastewater. The detention time of wastewater in the collection network is estimated as 12 to 24 h (Table S4). In Fig. 1 it is assumed that in most cases, wastewater laboratory tests are completed within a day upon sample collection. A compilation of all the above-mentioned timelines indicates that the lag time may be estimated to be between 3 and 4 weeks ( Fig. 1). This is expected to vary with different variants and different sampling ...
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... The detention time of wastewater in the collection network is estimated as 12 to 24 h (Table S4). In Fig. 1 it is assumed that in most cases, wastewater laboratory tests are completed within a day upon sample collection. A compilation of all the above-mentioned timelines indicates that the lag time may be estimated to be between 3 and 4 weeks ( Fig. 1). This is expected to vary with different variants and different sampling ...
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... RT-ddPCR in wastewater samples reached comparatively lower peaks in June 2021, which preceded the increase of COVID-19 incidences towards the end of July and August 2021 (Fig. 4). The following decrease of N1 and N2 gene concentrations after each peak was largely due to the termination of shedding events which last a few weeks as demonstrated in Fig. 1 and Table ...
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... COVID-19 cases with lag times of 3, 4, and 5 weeks were chosen to correlate with N1 and N2 gene concentrations in gc/L, gc/d, and gc/L of sanitary flow using the aforementioned models. The Root Mean Square Error (RMSE) and Pearson's coefficient between actual cases and predicted cases were calculated to estimate the performance of each model shown in Supplementary Tables S10 -S15. From the previous result in Section 3.2, a lag time of 5 weeks exhibits a stronger correlation, in agreement with the models in Table 3 and Tables S29 and S30, based on Pearson's r. ...
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... PEG measurements (Fig. S1) and its Pearson's correlation (Table S26) demonstrated that the N1 and N2 concentrations did not correlate with COVID-19 cases with a lag time. Results from the same models presented above are shown in Table S22 and S23 for PEG measurements. Results demonstrate that N1 and N2 concentrations based on PEG method did not provide an early ...
Citations
... There exist various mathematical and statistical predictive models for time series data [35][36][37][38]. We worked with three predictive models-autoregressive integrated moving average (ARIMA),distributed lag (DL), and autoregressive distributed lag (ADL)-for lag analysis based on two primary considerations: 1) their common usage in analogous studies [14,[38][39][40][41][42][43][44] and 2) their well-established frameworks, widespread application in time series analysis, and straightforward interpretability [45]. However, the models either failed to fit the data significantly or predicted lags that varied between models, indicating that our data set was not suitable for this type of predictive analysis. ...
Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.
... demonstrating the importance of applied wastewater surveillance in understanding virus transmission dynamics and for serving as an early warning system [1][2][3][4][5][6][7][8][9]. Wastewater surveillance approaches that extend beyond the surveillance of confirmed viral diseases in a community and extend to multiple reportable and non-reportable virus-related diseases are needed. ...
... Within the Detroit metropolitan area, wastewater surveillance has been applied to detect multiple human virus occurrences [12,16,17,20]. Since the onset of the COVID-19 epidemic in the Detroit metropolitan area, a wastewater surveillance program was focused on SARS-CoV-2 detection and has since shown to be an important tool in (1) providing early warnings of disease surges [6,21], (2) dissecting the spatial distribution of SARS-CoV-2 concentrations across a large geographic area in communities with diverse demographic characteristics [7], and (3) developing straightforward methods designed to assist public health officials in mounting a timely and appropriate response [22]. In this study, we investigate human virus diversity beyond coronaviruses. ...
... Viral nucleic acids were extracted using QIAGEN QIAamp Viral RNA kits (QIAGEN, Hilden, Germany), following the manufacturer's protocol with the volume of final eluting reagent (buffer AVE) modified from 60 to 140 µL [6,7,16]. To ensure enough sample for the final metagenomic library, extracts of duplicate samples were pooled together. ...
Background
Periodic bioinformatics-based screening of wastewater for assessing the diversity of potential human viral pathogens circulating in a given community may help to identify novel or potentially emerging infectious diseases. Any identified contigs related to novel or emerging viruses should be confirmed with targeted wastewater and clinical testing.
Results
During the COVID-19 pandemic, untreated wastewater samples were collected for a 1-year period from the Great Lakes Water Authority Wastewater Treatment Facility in Detroit, MI, USA, and viral population diversity from both centralized interceptor sites and localized neighborhood sewersheds was investigated. Clinical cases of the diseases caused by human viruses were tabulated and compared with data from viral wastewater monitoring. In addition to Betacoronavirus, comparison using assembled contigs against a custom Swiss-Prot human virus database indicated the potential prevalence of other pathogenic virus genera, including: Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Bocaparvovirus, Molluscipoxvirus, Parechovirus, Roseolovirus, Lymphocryptovirus, Alphavirus, Spumavirus, Lentivirus, Deltaretrovirus, Enterovirus, Kobuvirus, Gammaretrovirus, Cardiovirus, Erythroparvovirus, Salivirus, Rubivirus, Orthohepevirus, Cytomegalovirus, Norovirus, and Mamastrovirus. Four nearly complete genomes were recovered from the Astrovirus, Enterovirus, Norovirus and Betapolyomavirus genera and viral species were identified.
Conclusions
The presented findings in wastewater samples are primarily at the genus level and can serve as a preliminary “screening” tool that may serve as indication to initiate further testing for the confirmation of the presence of species that may be associated with human disease. Integrating innovative environmental microbiology technologies like metagenomic sequencing with viral epidemiology offers a significant opportunity to improve the monitoring of, and predictive intelligence for, pathogenic viruses, using wastewater.
... Modeling the COVID-19 virus in rural communities has been completed using various applications and methodologies [20][21][22][23][24][25]. Studies for other entities such as drugs have included rural communities as a case [26-28 as examples]. ...
Wastewater-based epidemiology (WBE) for the detection of agents of concern such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been prevalent in literature since 2020. The majority of reported research focuses on large urban centers with few references to rural communities. In this research the EPA-Storm Water Management Model (EPA-SWMM) software was used to describe a small sewershed and identify the effects of temperature, temperature-affected decay rate, flow rate, flush time, fecal shedding rate, and historical infection rates during the spread of the Omicron variant of the SARS-CoV-2 virus within the sewershed. Due to the sewershed's relative isolation from the rest of the city, its wastewater quality behavior is similar to a rural sewershed. The model was used to assess city wastewater sampling campaigns to best appropriate field and or lab equipment when sampling wastewater. An important aspect of the assessment was the comparison of SARS-CoV-2 quantification methods with specifically between a traditional microbiological lab (practical quantitation limit, PQL, 1 GC/mL) versus what can be known from a field method (PQL 10 GC/mL). Understanding these monitoring choices will help rural communities make decisions on how to best implement the collection and testing for WBE agents of concern. An important outcome of this work is the knowledge that it is possible to simulate a WBE agent of concern with reasonable precision, if uncertainties are incorporated into model sensitivity. These ideas could form the basis for future mixed monitoring-modeling studies that will enhance its application and therefore adoption of WBE techniques in communities of many sizes and financial means.
... However, the lead-time observed in this study demonstrated insufficient time to provide an early warning of SARS-CoV-2 transmission in a school before onward transmission from an index case can occur. Others have shown a greater lead time suggesting the possible use of wastewater surveillance as an early warning tool for the early stages of COVID-19 epidemics [22][23][24][25]. ...
Wastewater surveillance provides a cost-effective and non-invasive way to gain an understanding of infectious disease transmission including for COVID-19. We analyzed wastewater samples from one school site in Jefferson County, New York during the 2021–2022 school year. We tested for SARS-CoV-2 RNA once weekly and compared those results with the clinical COVID-19 cases in the school. The amount of SARS-CoV-2 RNA correlated with the number of incident COVID-19 cases, with the best correlation being one day lead time between the wastewater sample and the number of COVID-19 cases. The sensitivity and positive predictive value of wastewater surveillance to correctly identify any COVID-19 cases up to 7 days after a wastewater sample collection ranged from 82–100% and 59–78% respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The specificity and negative predictive value of wastewater surveillance to correctly identify when the school was without a case of COVID-19 ranged from 67–78% and 70–80%, respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The lead time observed in this study suggests that transmission might occur within a school before SARS-CoV-2 is identified in wastewater. However, wastewater surveillance should still be considered as a potential means of understanding school-level COVID-19 trends and is a way to enable precision public health approaches tailored to the epidemiologic situation in an individual school.
... Wastewater surveillance has gained immense attention since the inception of the COVID-19 pandemic and has been widely utilized to monitor the disease globally (Ahmed et al. 2020(Ahmed et al. , 2021Bivins and Bibby 2021;Galani et al. 2022;Gentry et al. 2023;Hopkins et al. 2023;Li et al. 2022;Miyani et al. 2020Miyani et al. , 2021Peccia et al. 2020;Saguti et al. 2021;Schenk et al. 2023;Zhao et al. 2022Zhao et al. , 2023b. One of the most intensely studied and prominent applications of wastewater surveillance is the determination of the time lag between Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater concentrations and COVID-19 clinical metrics, primarily confirmed COVID-19 cases (Miyani et al. 2021;Peccia et al. 2020;Zhao et al. 2022). ...
... Wastewater surveillance has gained immense attention since the inception of the COVID-19 pandemic and has been widely utilized to monitor the disease globally (Ahmed et al. 2020(Ahmed et al. , 2021Bivins and Bibby 2021;Galani et al. 2022;Gentry et al. 2023;Hopkins et al. 2023;Li et al. 2022;Miyani et al. 2020Miyani et al. , 2021Peccia et al. 2020;Saguti et al. 2021;Schenk et al. 2023;Zhao et al. 2022Zhao et al. , 2023b. One of the most intensely studied and prominent applications of wastewater surveillance is the determination of the time lag between Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater concentrations and COVID-19 clinical metrics, primarily confirmed COVID-19 cases (Miyani et al. 2021;Peccia et al. 2020;Zhao et al. 2022). Recently, a few studies investigated the time lag between SARS-CoV-2 wastewater surveillance and other COVID-19 clinical metrics, including COVID-19 hospitalizations and intensive care unit (ICU) admissions. ...
... In this study, "time lag" was defined as the duration between peaks in measured SARS-CoV-2 wastewater concentrations and peaks in reported clinical metrics (Zhao et al. 2022). We investigated the time lag between SARS-CoV-2 N1 and N2 gene concentrations in wastewater and three clinical metrics, including confirmed cases, hospitalizations, and ICU admissions, all within the Tricounty Detroit area (TCDA), Michigan, US, between September 1, 2020, and May 31, 2022. ...
Abstract: Wastewater surveillance has been widely implemented to monitor COVID-19 incidences in communities worldwide. One notable application of wastewater surveillance is for providing early warnings of disease outbreaks. Many studies have reported time lags between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater concentrations and confirmed clinical COVID-19 cases. To our best knowledge, only a few studies to date have explored time lags between SARS-CoV-2 wastewater concentrations and other clinical metrics. In this study, we investigated time lags between SARS-CoV-2 wastewater concentrations and three COVID-19 clinical metrics: confirmed clinical cases, hospitalizations, and intensive care unit (ICU) admissions, in the Tricounty Detroit Area, Michigan, US. The COVID-19 clinical metrics were dated between September 1, 2020, and October 31, 2022, and were collected from public data sources. SARS-CoV-2 N1 and N2 gene concentrations between September 1, 2020, and May 31, 2022, were generated using two sampling and concentration methods: virus adsorption-elution (VIRADEL) and polyethylene glycol precipitation (PEG). The data were collected from our recently published study. Time-lagged cross correlation was implemented to estimate time lags between gene concentrations and the three clinical metrics. Original gene concentrations were normalized by wastewater flow parameters through nine approaches to estimate the impact of wastewater flow on time lags. Vector autoregression models were established to analyze the relationship between gene concentrations and clinical metrics. The results indicate that VIRADEL gene concentrations in wastewater preceded all clinical metrics prior to the COVID-19 Omicron surge, for instance, 32, 47, and 51 days preceding confirmed cases, hospitalizations, and ICU admissions, respectively (gene concentrations unit: gc=day). When translated to a public health context, these time lags become critical lead times for officials to prepare and react. During the Omicron surge, there were significant reductions in time lags, with VIRADEL measurements trailing total ICU admissions. PEG measurements lagged behind the three clinical metrics and did not provide
early warnings of disease surges.
DOI: 10.1061/JOEEDU.EEENG-7509. © 2023 American Society of Civil Engineers.
... Wastewater surveillance (WWS) efforts for monitoring active COVID-19-positive cases are ongoing worldwide and are playing a major role in the early detection of community outbreaks (Randazzo et al. 2020a;Bivins et al. 2020;Medema et al. 2020;Ahmed et al. 2020a;D'Aoust et al. 2021a;La Rosa et al. 2021;McClary-Gutierrez et al. 2021;Zhao et al. 2022;Jiang et al. 2023). The utilization of solids-based viral extraction protocols for SARS-CoV-2 (ESM Table (1A) in List of Abbreviations) detection in wastewater has proven to be highly effective particularly in samples with a high solids content, such as primary sludge, raw wastewater influent, and municipal wastewaters within sewer systems (Balboa et al. 2021;Peccia et al. 2020;D'Aoust et al. 2021b;Graham et al. 2021;Petala et al. 2021;Westhaus et al. 2021;Espinosa et al. ...
Wastewater surveillance (WWS) of SARS-CoV-2 has become a crucial tool for monitoring COVID-19 cases and outbreaks. Previous studies have indicated that SARS-CoV-2 RNA measurement from testing solid-rich primary sludge yields better sensitivity compared to testing wastewater influent. Furthermore, measurement of pepper mild mottle virus (PMMoV) signal in wastewater allows for precise normalization of SARS-CoV-2 viral signal based on solid content, enhancing disease prevalence tracking. However, despite the widespread adoption of WWS, a knowledge gap remains regarding the impact of ferric sulfate coagulation, commonly used in enhanced primary clarification, the initial stage of wastewater treatment where solids are sedimented and removed, on SARS-CoV-2 and PMMoV quantification in wastewater-based epidemiology. This study examines the effects of ferric sulfate addition, along with the associated pH reduction, on the measurement of SARS-CoV-2 and PMMoV viral measurements in wastewater primary clarified sludge through jar testing. Results show that the addition of Fe3+ concentrations in the conventional 0 to 60 mg/L range caused no effect on SARS-CoV-2 N1 and N2 gene region measurements in wastewater solids. However, elevated Fe3+ concentrations were shown to be associated with a statistically significant increase in PMMoV viral measurements in wastewater solids, which consequently resulted in the underestimation of PMMoV-normalized SARS-CoV-2 viral signal measurements (N1 and N2 copies/copies of PMMoV). The observed pH reduction from coagulant addition did not contribute to the increased PMMoV measurements, suggesting that this phenomenon arises from the partitioning of PMMoV viral particles into wastewater solids.
... We also tested samples for Pepper Mild Mottle Virus (PMMoV)a standard control in USA laboratories 33 but after testing multiple water and fecal samples and using 2 different primer/probe sets 42,53 (F:GAGTGGTTTGACCTTAACGTTTGA, R:TTGTCGGTTGCAATGCAAGT, and P:/5Cy5/CCTACCGAAGCAAATG/3IAbRQSp/ and F:GCTGAAGG TTGGTACTTGTA, R:TCAGGTCGGCTATGTATCAT, P:5Cy5/TGGATGAG CAGCGAACGGGTGA/3IAbRQSp/) we found zero positive sample. The same samples were positive for SARS-CoV-2 and Hf183 so there are both detected virus and bacteria in the samples. ...
The COVID-19 pandemic has profoundly impacted health systems globally and robust surveillance has been critical for pandemic control, however not all countries can currently sustain community pathogen surveillance programs. Wastewater surveillance has proven valuable in high-income settings, but less is known about the utility of water surveillance of pathogens in low-income countries. Here we show how wastewater surveillance of SAR-CoV-2 can be used to identify temporal changes and help determine circulating variants quickly. In Malawi, a country with limited community-based COVID-19 testing capacity, we explore the utility of rivers and wastewater for SARS-CoV-2 surveillance. From May 2020–May 2022, we collect water from up to 112 river or defunct wastewater treatment plant sites, detecting SARS-CoV-2 in 8.3% of samples. Peak SARS-CoV-2 detection in water samples predate peaks in clinical cases. Sequencing of water samples identified the Beta, Delta, and Omicron variants, with Delta and Omicron detected well in advance of detection in patients. Our work highlights how wastewater can be used to detect emerging waves, identify variants of concern, and provide an early warning system in settings with no formal sewage systems.
... This method has been used to identify emerging variants up to 14 days earlier than clinical genomic surveillance 97 in multiple studies around the world. [97][98][99] Recent technological advances in nucleic acid sequencing and computational tools allow sequencing with near 95% genome coverage of viruses in wastewater samples, 97 while only 40% coverage was possible in previous studies. [100][101][102] Full-length SARS-CoV-2 variant genomic sequence data generated by wastewater sampling provides a comprehensive picture of all viral sequences present in a population, including emerging variants, as opposed to clinical samples that typically only give data on a single virus variant from a single individual. ...
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern, first identified in November 2021, rapidly spread worldwide and diversified into several subvariants. The Omicron spike (S) protein accumulated an unprecedented number of sequence changes relative to previous variants. In this review, we discuss how Omicron S protein structural features modulate host cell receptor binding, virus entry, and immune evasion and highlight how these structural features differentiate Omicron from previous variants. We also examine how key structural properties track across the still-evolving Omicron subvariants and the importance of continuing surveillance of the S protein sequence evolution over time.
... Their model incorporates uniquely a set of widely accessible measures such as demography, contact tracing, wifi-based location data, viral load and COVID testing data. Zhao et al. (2022) developed four statistical modelling approach: liner regression, autoregressive integrated moving average (ARIMA), seasonal ARIMA, vector autoregressive model using wastewater samples from 407 WWTPs over a 12 month period. They have shown that seasonal ARIMA and vector autoregressive models had an optimized prediction for COVID-19 cases. ...
... Eight of the 15 studies that we reviewed in detail used an auto-sampler from wastewater treatment plants [32,35,40,41,43,60,61,64]. Only two of these gave details of the number of sampling rounds and the number of samples per round: Krivoňáková ...
... Furthermore, the harmonisation of sampling and measurement protocols holds promise for enhancing the comparability and reproducibility of findings from wastewater studies (Kasprzyk-Hordern et al., 2022). Our review has revealed considerations across all studies for various contributing factors in the multi-dimensional nature of the wastewater sampling and testing process, as discussed in Olesen et al. (2021) and Zhao et al. (2022). Nevertheless, adopting a comprehensive framework that accounts for sampling, testing and reporting variables in a spatio-temporal manner would bring us closer to a harmonised approach. ...
The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.
... One critical aspect is the temporal relationship between SARS-CoV-2 wastewater loads measured at a wastewater treatment plant (WWTP) and reported COVID-19 cases in the corresponding sewershed served by the plant [5,19]. Past work has demonstrated that increases in SARS-CoV-2 wastewater loads may occur prior to a rise in lab-confirmed sewershed COVID-19 cases in a sewershed, allowing for WBE to be used as an early warning system [4,[20][21][22]. Such leading signals in wastewater were reported during the earlier phases of the pandemic in some North Carolina sewersheds [10,23] as well as during more recent pandemic phases [24]. ...
... As the pandemic becomes endemic, trends lasting several months have been widely reported to anticipate trends in COVID-19 infections, as later indicated by population surveillance metrics [21,22,25,26]. However, the time alignment between trends in wastewater load and trends in cases can be difficult to determine since its small temporal lead or lag may be eclipsed by the longer time scale of trends. ...
Wastewater surveillance emerged during the COVID-19 pandemic as a novel strategy for tracking the burden of illness in communities. Previous work has shown that trends in wastewater SARS-CoV-2 viral loads correlate well with reported COVID-19 case trends over longer time periods (i.e., months). We used detrending time series to reveal shorter sub-trend patterns (i.e., weeks) to identify leads or lags in the temporal alignment of the wastewater/case relationship. Daily incident COVID-19 cases and twice-weekly wastewater SARS-CoV-2 viral loads measured at 20 North Carolina sewersheds in 2021 were detrended using smoothing ranges of ∞, 16, 8, 4 and 2 weeks, to produce detrended cases and wastewater viral loads at progressively finer time scales. For each sewershed and smoothing range, we calculated the Spearman correlation between the cases and the wastewater viral loads with offsets of -7 to +7 days. We identified a conclusive lead/lag relationship at 15 of 20 sewersheds, with detrended wastewater loads temporally leading detrended COVID-19 cases at 11 of these sites. For the 11 leading sites, the correlation between wastewater loads and cases was greatest for wastewater loads sampled at a median lead time of 6 days before the cases were reported. Distinct lead/lag relationships were the most pronounced after detrending with smoothing ranges of 4–8 weeks, suggesting that SARS-CoV-2 wastewater viral loads can track fluctuations in COVID-19 case incidence rates at fine time scales and may serve as a leading indicator in many settings. These results could help public health officials identify, and deploy timely responses in, areas where cases are increasing faster than the overall pandemic trend.
