SARS-CoV-2 detection in wastewater as an early warning indicator for COVID-19 pandemic. Madrid region case study

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

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.

Development of an early alert model for pandemic situations in Germany

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

Presence of SARS-CoV-2 virus in wastewater in the Kingdom of Bahrain during the COVID-19 pandemic

Background

Several countries, including Bahrain, used wastewater surveillance for disease activity monitoring. This study aimed to determine the presence of SARS-CoV-2 in untreated wastewater and to correlate it with the disease spread.

Methods

A retrospective review was conducted for all wastewater samples tested for SARS-CoV-2 in public health laboratories from November 2020 to October 2022. Samples were collected weekly between February and October 2022 from different areas across Bahrain. Real-time polymerase chain reaction was used to test for the presence of SARS-CoV-2 in wastewater, and the results were correlated with the number of COVID-19 cases in the same area.

Results

Of 387 wastewater samples, 103 (26.6%) samples tested positive for SARS-CoV-2. In late 2020, of 42 samples collected initially, four (9.5%) samples tested positive for SARS-CoV-2 in the four locations that hosted COVID-19 isolation facilities. Between February and October 2022, 345 specimens of wastewater were tested, and 99 (28.7%) were positive. The highest detection rate was in February, June, and July (60%, 45%, and 43%, respectively), which corresponded to COVID-19 peaks during 2022, and the lowest detection rate was in August and September (11% and 0%, respectively), corresponding to the low number of COVID-19 cases.

Conclusion

The detection rate of SARS-CoV-2 in wastewater samples from Bahrain was high and was significantly correlated with the number of reported COVID-19 cases. Wastewater surveillance can aid the existing surveillance system in monitoring SARS-CoV-2 spread.

Measures against COVID-19 affected the spread of human enteric viruses in a Swedish community, as found when monitoring wastewater

The quantification of viral genomes in wastewater reflects the prevalence of viral infections within the community. Knowledge of how the spread of common enteric viruses in the community was affected by the Swedish COVID-19 interventions is limited. To investigate this, the weekly wastewater samples collected for monitoring SARS-CoV-2 throughout the COVID-19 pandemic at the Rya sewage treatment plant in Gothenburg were also analyzed for adenovirus, norovirus GII, astrovirus, and rotavirus. The amount of each viral genome was quantified by real-time-qPCR and compared with the quantity of these viral genomes in wastewater from 2017. The results showed that the winter seasonality of norovirus GII and rotavirus in wastewater observed in 2017 was interrupted shortly after the introduction of the COVID-19 interventions, and they remained at low level throughout the pandemic. The circulation pattern of astrovirus and adenovirus was less affected. When the COVID-19 restrictions were lifted in 2022, a dramatic increase was observed in the amount of norovirus GII, rotavirus, and adenovirus genomes in wastewater. The changes in abundance and seasonality of some viruses identified through wastewater monitoring were consistent with changes in the number of patients diagnosed with these viruses. These findings suggest that moderate intervention to prevent COVID-19 significantly reduced the spread of some enteric viruses in the community. The results show that wastewater monitoring is a valuable tool for detecting the spread and outbreaks of viral infections that may cause gastroenteritis also when people do not seek medical help, such as during the COVID-19 pandemic.

SARS-CoV-2 RNA in Wastewater and Bivalve Mollusk Samples of Campania, Southern Italy

SARS-CoV-2 can be detected in the feces of infected people, consequently in wastewater, and in bivalve mollusks, that are able to accumulate viruses due to their ability to filter large amounts of water. This study aimed to monitor SARS-CoV-2 RNA presence in 168 raw wastewater samples collected from six wastewater treatment plants (WWTPs) and 57 mollusk samples obtained from eight harvesting sites in Campania, Italy. The monitoring period spanned from October 2021 to April 2022, and the results were compared and correlated with the epidemiological situation. In sewage, the ORF1b region of SARS-CoV-2 was detected using RT-qPCR, while in mollusks, three targets-RdRp, ORF1b, and E-were identified via RT-dPCR. Results showed a 92.3% rate of positive wastewater samples with increased genomic copies (g.c.)/(day*inhabitant) in December-January and March-April 2022. In the entire observation period, 54.4% of mollusks tested positive for at least one SARS-CoV-2 target, and the rate of positive samples showed a trend similar to that of the wastewater samples. The lower SARS-CoV-2 positivity rate in bivalve mollusks compared to sewages is a direct consequence of the seawater dilution effect. Our data confirm that both sample types can be used as sentinels to detect SARS-CoV-2 in the environment and suggest their potential use in obtaining complementary information on SARS-CoV-2.

Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates

Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.

Understanding the efficacy of wastewater surveillance for SARS-CoV-2 in two diverse communities

During the COVID-19 pandemic, wastewater-based surveillance has been shown to be a useful tool for monitoring the spread of disease in communities and the emergence of new viral variants of concern. As the pandemic enters its fourth year and clinical testing has declined, wastewater offers a consistent non-intrusive way to monitor community health in the long term. This study sought to understand how accurately wastewater monitoring represented the actual burden of disease between communities. Two communities varying in size and demographics in Michigan were monitored for SARS-CoV-2 in wastewater between March of 2020 and February of 2022. Additionally, each community was monitored for SARS-CoV-2 variants of concern from December 2020 to February 2022. Wastewater results were compared with zipcode and county level COVID-19 case data to determine which scope of clinical surveillance was most correlated with wastewater loading. Pearson r correlations were highest in the smaller of the two communities (population of 25,000) for N1 GC/person/day with zipcode level case data, and date of the onset of symptoms (r = 0.81). A clear difference was seen with more cases and virus signals in the wastewater of the larger community (population 110,000) when examined based on vaccine status, which reached only 50%. While wastewater levels of SARS-CoV-2 had a lower correlation to cases in the larger community, the information was still seen as valuable in supporting public health actions and further data including vaccination status should be examined in the future.

Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).

Long-term SARS-CoV-2 surveillance in the wastewater of Stockholm: What lessons can be learned from the Swedish perspective?

Wastewater-based epidemiology (WBE) can be used to track the spread of SARS-CoV-2 in a population. This study presents the learning outcomes from over two-year long monitoring of SARS-CoV-2 in Stockholm, Sweden. The three main wastewater treatment plants in Stockholm, with a total of six inlets, were monitored from April 2020 until June 2022 (in total 600 samples). This spans five major SARS-CoV-2 waves, where WBE data provided early warning signals for each wave. Further, the measured SARS-CoV-2 content in the wastewater correlated significantly with the level of positive COVID-19 tests (r = 0.86; p << 0.0001) measured by widespread testing of the population. Moreover, as a proof-of-concept, six SARS-CoV-2 variants of concern were monitored using hpPCR assay, demonstrating that variants can be traced through wastewater monitoring. During this long-term surveillance, two sampling protocols, two RNA concentration/extraction methods, two calculation approaches, and normalization to the RNA virus Pepper mild mottle virus (PMMoV) were evaluated. In addition, a study of storage conditions was performed, demonstrating that the decay of viral RNA was significantly reduced upon the addition of glycerol to the wastewater before storage at -80 °C. Our results provide valuable information that can facilitate the incorporation of WBE as a prediction tool for possible future outbreaks of SARS-CoV-2 and preparations for future pandemics.

SARS-CoV-2 infection dynamics revealed by wastewater sequencing analysis and deconvolution

The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Wastewater-based epidemiological surveillance to monitor the prevalence of SARS-CoV-2 in developing countries with onsite sanitation facilities

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.

Wastewater-based epidemiology (WBE) for SARS-CoV-2 - A review focussing on the significance of the sewer network using a Dublin city catchment case study

Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process.

The amount of SARS-CoV-2 RNA in wastewater relates to the development of the pandemic and its burden on the health system

Virus surveillance in wastewater can be a useful indicator of the development of the COVID-19 pandemic in communities. However, knowledge about how the amount of SARS-CoV-2 RNA in wastewater relates to different data on the burden on the health system is still limited. Herein, we monitored the amount of SARS-CoV-2 RNA and the spectrum of virus variants in weekly pooled wastewater samples for two years from mid-February 2020 and compared with several clinical data. The two-year monitoring showed the weekly changes in the amount of viral RNA in wastewater preceded the hospital care needs for COVID-19 and the number of acute calls on adult acute respiratory distress by 1-2 weeks during the first three waves of COVID-19. Our study demonstrates that virus surveillance in wastewater can predict the development of a pandemic and its burden on the health system, regardless of society's test capacity and possibility of tracking infected cases.

Wastewater and marine bioindicators surveillance to anticipate COVID-19 prevalence and to explore SARS-CoV-2 diversity by next generation sequencing: One-year study

This study presents the results of SARS-CoV-2 surveillance in sewage water of 11 municipalities and marine bioindicators in Galicia (NW of Spain) from May 2020 to May 2021. An integrated pipeline was developed including sampling, pre-treatment and biomarker quantification, RNA detection, SARS-CoV-2 sequencing, mechanistic mathematical modeling and forecasting. The viral load in the inlet stream to the wastewater treatment plants (WWTP) was used to detect new outbreaks of COVID-19, and the data of viral load in the wastewater in combination with data provided by the health system was used to predict the evolution of the pandemic in the municipalities under study within a time horizon of 7 days. Moreover, the study shows that the viral load was eliminated from the treated sewage water in the WWTP, mainly in the biological reactors and the disinfection system. As a result, we detected a minor impact of the virus in the marine environment through the analysis of seawater, marine sediments and, wild and aquacultured mussels in the final discharge point of the WWTP.

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

Acquired insights from the long-term surveillance of SARS-CoV-2 RNA for COVID-19 monitoring: The case of Monterrey Metropolitan Area (Mexico)

Wastewater-based epidemiology offers a time- and cost-effective way to monitor SARS-CoV-2 spread in communities and therefore represents a complement to clinical testing. WBE applicability has been demonstrated in a number of cases over short-term periods as a method for tracking the prevalence of SARS-CoV-2 and an early-warning tool for predicting outbreaks in the population. This study reports SARS-CoV-2 viral loads from wastewater treatment plants (WWTPs) and hospitals over a 6-month period (June to December 2020). Results show that the overall range of viral load in positive tested samples was between 1.2 × 10³ and 3.5 × 10⁶ gene copies/l, unveiling that secondary-treated wastewaters mirrored the viral load of influents. The interpretation suggests that the viral titers found in three out of four WWTPs were associated to clinical COVID-19 surveillance indicators preceding 2-7 days the rise of reported clinical cases. The median wastewater detection rate of SARS-CoV-2 was one out of 14,300 reported new cases. Preliminary model estimates of prevalence ranged from 0.02 to 4.6% for the studied period. This comprehensive statistical and epidemiological analysis demonstrates that the applied wastewater-based approach to COVID-19 surveillance is in general consistent and feasible, although there is room for improvements.

Peroxymonosulphate Activation by Basolite® F-300 for Escherichia coli Disinfection and Antipyrine Degradation

In this study, the removal of persistent emerging and dangerous pollutants (pharmaceuticals and pathogens) in synthetic wastewater was evaluated by the application of heterogeneous Advanced Oxidation Processes. To do that, a Metal-Organic Framework (MOF), Basolite® F-300 was selected as a catalyst and combined with peroxymonosulfate (PMS) as oxidants in order to generate sulphate radicals. Several key parameters such as the PMS and Basolite® F-300 concentration were evaluated and optimized using a Central Composite Experimental Design for response surface methodology for the inactivation of Escherichia coli. The assessment of the degradation of an analgesic and antipyretic pharmaceutical, antipyrine, revealed that is necessary to increase the concentration of PMS and amount of Basolite® F-300, in order to diminish the treatment time. Finally, the PMS-Basolite® F-300 system can be used for at least four cycles without a reduction in its ability to disinfect and degrade persistent emerging and dangerous pollutants such as pharmaceuticals and pathogens.

Space-time analysis of COVID-19 cases and SARS-CoV-2 wastewater loading: A geodemographic perspective

Severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) continues to effect communities across the world. One way to combat these effects is to enhance our collective ability to remotely monitor community spread. Monitoring SARS-CoV-2 in wastewater is one approach that enables researchers to estimate the total number of infected people in a region; however, estimates are often made at the sewershed level which may mask the geographic nuance required for targeted interdiction efforts. In this work, we utilize an apportioning method to compare the spatial and temporal trends of daily case count with the temporal pattern of viral load in the wastewater at smaller units of analysis within Austin, TX. We find different lag-times between wastewater loading and case reports. Daily case reports for some locations follow the temporal trend of viral load more closely than others. These findings are then compared to socio-demographic characteristics across the study area.

Relating SARS-CoV-2 shedding rate in wastewater to daily positive tests data: A consistent model based approach

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been engaged to complement medical surveillance and in some cases to also act as an early diagnosis indicator of viral spreading in the community. Most efforts worldwide by the scientific community and commercial companies focus on the formulation of protocols for SARS-CoV-2 analysis in wastewater and approaches addressing the quantitative relationship between WBE and medical surveillance are lacking. In the present study, a mathematical model is developed which uses as input the number of daily positive medical tests together with the highly non-linear shedding rate curve of individuals to estimate the evolution of global virus shedding rate in wastewater along calendar days. A comprehensive parametric study by the model using as input actual medical surveillance and WBE data for the city of Thessaloniki (~700,000 inhabitants, North Greece) during the outbreak of November 2020 reveals the conditions under which WBE can be used as an early warning tool for predicting pandemic outbreaks. It is shown that early warning capacity is different along the days of an outbreak and depends strongly on the number of days apart between the day of maximum shedding rate of infected individuals in their disease cycle and the day of their medical testing. The present data indicate for Thessaloniki an average early warning capacity of around 2 days. Moreover, the data imply that there exists a proportion between unreported cases (asymptomatic persons with mild symptoms that do not seek medical advice) and reported cases. The proportion increases with the number of reported cases. The early detection capacity of WBE improves substantially in the presence of an increasing number of unreported cases. For Thessaloniki at the peak of the pandemic in mid-November 2020, the number of unreported cases reached a maximum around 4 times the number of reported cases.

Detection of SARS-CoV-2 in the sewerage system in Tunisia: a promising tool to confront COVID-19 pandemic

Aim: The current study undertaken in Tunisia examines the use of wastewaters to monitor SARS-CoV-2 circulation. Materials & methods: Viral genetic materials collected in wastewaters during two different periods (September-October 2020 and February-April 2021) were concentrated using the adsorption-elution method. SARS-CoV-2 genes were researched by real-time PCR. Results: During the first period of the study, viral RNA was detected in 61.11% of the analyzed samples collected from Monastir city with a rate of 88.88% for raw wastewaters and 33.33% for treated wastewaters. Then, during the second period of the study, the quantitative analysis of wastewaters collected from seven governorates showed the presence of viral RNA among around 25% of them with variable RNA loads. The increased amounts of viral RNA detected in wastewaters were accompanied by an increase in the number of COVID-19 patients in Tunisia. Conclusion: Our results emphasize the importance of sewage survey in SARS-CoV-2 tracking.