Brief Report: Under-Identification of Symptomatic Menopause in Publicly-Insured Autistic People.

Menopause is a normal part of aging and in the general population is associated with chronic conditions that impact health, mortality, and well-being. Menopause is experienced differently by autistic individuals, although no studies have investigated this topic in a large sample. The purpose of this study was to investigate rates of, and factors associated with symptomatic menopause among autistic individuals and to identify the prevalence of co-occurring conditions in symptomatic individuals. We included autistic females aged 35-70 years enrolled for 10 + months in 2014-2016 Medicare and/or Medicaid (n = 26,904), excluding those with gender dysphoria. Those with symptomatic menopause were compared to a non-symptomatic reference group on demographic, enrollment characteristics, and co-occurring conditions through logistic regression. Approximately 4% of publicly-insured autistic females aged 46-70 years had symptomatic menopause in their medical records. Intellectual disability was associated with a lower likelihood of symptomatic menopause, and being Medicare-enrolled or dual-enrolled was associated with higher likelihood of having symptomatic menopause recorded. In adjusted models, rates of ADHD, anxiety and depressive disorders, headache/migraine, altered sensory experiences, altered sexual function, and sleep disturbance were significantly higher in the symptomatic menopause sample compared to the reference group. More work to better support autistic women in discussing menopausal symptoms and co-occurring conditions with primary care providers is needed, particularly among those for whom self-report of symptoms are more challenging to ascertain. Factors associated with specific types of health care coverage warrant greater investigation to support better identification.

An RNA modification enzyme directly senses reactive oxygen species for translational regulation in Enterococcus faecalis.

Bacteria possess elaborate systems to manage reactive oxygen and nitrogen species (ROS) arising from exposure to the mammalian immune system and environmental stresses. Here we report the discovery of an ROS-sensing RNA-modifying enzyme that regulates translation of stress-response proteins in the gut commensal and opportunistic pathogen Enterococcus faecalis. We analyze the tRNA epitranscriptome of E. faecalis in response to reactive oxygen species (ROS) or sublethal doses of ROS-inducing antibiotics and identify large decreases in N2-methyladenosine (m2A) in both 23 S ribosomal RNA and transfer RNA. This we determine to be due to ROS-mediated inactivation of the Fe-S cluster-containing methyltransferase, RlmN. Genetic knockout of RlmN gives rise to a proteome that mimics the oxidative stress response, with an increase in levels of superoxide dismutase and decrease in virulence proteins. While tRNA modifications were established to be dynamic for fine-tuning translation, here we report the discovery of a dynamically regulated, environmentally responsive rRNA modification. These studies lead to a model in which RlmN serves as a redox-sensitive molecular switch, directly relaying oxidative stress to modulating translation through the rRNA and the tRNA epitranscriptome, adding a different paradigm in which RNA modifications can directly regulate the proteome.

Co-incidence of BA.1 and BA.2 at the start of Singapore's Omicron wave revealed by Community and University Campus wastewater surveillance.

Wastewater surveillance (WWS) has been globally recognised to be a useful tool in quantifying SARS-CoV-2 RNA at the community and residential levels without biases associated with case-reporting. The emergence of variants of concern (VOCs) have given rise to an unprecedented number of infections even though populations are increasingly vaccinated. This is because VOCs have been reported to possess higher transmissibility and can evade host immune responses. The B.1.1.529 lineage (Omicron) has severely disrupted global plans to return to normalcy. In this study, we developed an allele-specific (AS) RT-qPCR assay which simultaneously targets the stretch of deletions and mutations in the spike protein from position 24-27 for quantitative detection of Omicron BA.2. Together with previous assays that detect mutations associated with Omicron BA.1 (deletion at position 69 and 70) and all Omicron (mutation at position 493 and 498), we report the validation and time series of these assays from September 2021 to May 2022 using influent samples from two wastewater treatment plants and across four University campus sites in Singapore. Viral RNA concentrations at the treatment plants corroborate with locally reported clinical cases, AS RT-qPCR assays revealed co-incidence of Omicron BA.1 and BA.2 on 12 January 2022, almost two months after initial BA.1 detection in South Africa and Botswana. BA.2 became the dominant variant by the end of January 2022 and completely displaced BA.1 by mid-March 2022. University campus sites were similarly positive for BA.1 and/or BA.2 in the same week as first detection at the treatment plants, where BA.2 became rapidly established as the dominant lineage within three weeks. These results corroborate clinical incidence of the Omicron lineages in Singapore and indicate minimal silent circulation prior to January 2022. The subsequent simultaneous spread of both variant lineages followed strategic relaxation of safe management measures upon meeting nationwide vaccination goals.

Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era.

SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.

Monitoring human arboviral diseases through wastewater surveillance: Challenges, progress and future opportunities.

Arboviral diseases are caused by a group of viruses spread by the bite of infected arthropods. Amongst these, dengue, Zika, west nile fever and yellow fever cause the greatest economic and social impact. Arboviral epidemics have increased in frequency, magnitude and geographical extent over the past decades and are expected to continue increasing with climate change and expanding urbanisation. Arboviral prevalence is largely underestimated, as most infections are asymptomatic, nevertheless existing surveillance systems are based on passive reporting of loosely defined clinical syndromes with infrequent laboratory confirmation. Wastewater-based surveillance (WBS), which has been demonstrated to be useful for monitoring diseases with significant asymptomatic populations including COVID19 and polio, could be a useful complement to arboviral surveillance. We review the current state of knowledge and identify key factors that affect the feasibility of monitoring arboviral diseases by WBS to include viral shedding loads by infected persons, the persistence of shed arboviruses and the efficiency of their recovery from sewage. We provide a simple model on the volume of wastewater that needs to be processed for detection of arboviruses, in face of lower arboviral shedding rates. In all, this review serves to reflect on the key challenges that need to be addressed and overcome for successful implementation of arboviral WBS.

Rapid displacement of SARS-CoV-2 variant Delta by Omicron revealed by allele-specific PCR in wastewater.

On November 26, 2021, the B.1.1.529 COVID-19 variant was classified as the Omicron variant of concern (VOC). Reports of higher transmissibility and potential immune evasion triggered flight bans and heightened health control measures across the world to stem its distribution. Wastewater-based surveillance has demonstrated to be a useful complement for clinical community-based tracking of SARS-CoV-2 variants. Using design principles of our previous assays that detect SARS-CoV-2 variants (Alpha and Delta), we developed an allele-specific RT-qPCR assay which simultaneously targets the stretch of mutations from Q493R to Q498R for quantitative detection of the Omicron variant in wastewater. We report their validation against 10-month longitudinal samples from the influent of a wastewater treatment plant in Italy. SARS-CoV-2 RNA concentrations and variant frequencies in wastewater determined using these variant assays agree with clinical cases, revealing rapid displacement of the Delta variant by the Omicron variant within three weeks. These variant trends, when mapped against vaccination rates, support clinical studies that found the rapid emergence of SARS-CoV-2 Omicron variant being associated with an infection advantage over Delta in vaccinated persons. These data reinforce the versatility, utility and accuracy of these open-sourced methods using allele-specific RT-qPCR for tracking the dynamics of variant displacement in communities through wastewater for informed public health responses.

Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future.

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics.

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Development of an efficient wastewater testing protocol for high-throughput country-wide SARS-CoV-2 monitoring.

Wastewater-based surveillance has been widely used as a non-intrusive tool to monitor population-level transmission of COVID-19. Although various approaches are available to concentrate viruses from wastewater samples, scalable methods remain limited. Here, we sought to identify and evaluate SARS-CoV-2 virus concentration protocols for high-throughput wastewater testing. A total of twelve protocols for polyethylene glycol (PEG) precipitation and four protocols for ultrafiltration-based approaches were evaluated across two phases. The first phase entailed an initial evaluation using a small sample set, while the second phase further evaluated five protocols using wastewater samples of varying SARS-CoV-2 concentrations. Permutations in the pre-concentration, virus concentration and RNA extraction steps were evaluated. Among PEG-based methods, SARS-CoV-2 virus recovery was optimal with 1) the removal of debris prior to processing, 2) 2 h to 24 h incubation with 8% PEG at 4 °C, 3) 4000 xg or 14,000 xg centrifugation, and 4) a column-based RNA extraction method, yielding virus recovery of 42.4-52.5%. Similarly, the optimal protocol for ultrafiltration included 1) the removal of debris prior to processing, 2) ultrafiltration, and 3) a column-based RNA extraction method, yielding a recovery of 38.2%. This study also revealed that SARS-CoV-2 RNA recovery for samples with higher virus concentration were less sensitive to changes in the PEG method, but permutations in the PEG protocol could significantly impact virus yields when wastewater samples with lower SARS-CoV-2 RNA were used. Although both PEG precipitation and ultrafiltration methods resulted in similar SARS-CoV-2 RNA recoveries, the former method is more cost-effective while the latter method provided operational efficiency as it required a shorter turn-around-time (PEG precipitation, 9-23 h; Ultrafiltration, 5 h). The decision on which method to adopt will thus depend on the use-case for wastewater testing, and the need for cost-effectiveness, sensitivity, operational feasibility and scalability.

Gut Ruminococcaceae levels at baseline correlate with risk of antibiotic-associated diarrhea.

Antibiotic-associated diarrhea (AAD) affects a significant proportion of patients receiving antibiotics. We sought to understand if differences in the gut microbiome would influence the development of AAD. We administered a 3-day course of amoxicillin-clavulanate to 30 healthy adult volunteers, and analyzed their stool microbiome, using 16S rRNA gene sequencing, at baseline and up to 4 weeks post antibiotic administration. Lower levels of gut Ruminococcaceae were significantly and consistently observed from baseline until day 7 in participants who developed AAD. Overall, participants who developed AAD experienced a greater decrease in microbial diversity. The probability of AAD could be predicted based on qPCR-derived levels of Faecalibacterium prausnitzii at baseline. Our findings suggest that a lack of gut Ruminococcaceae influences development of AAD. Quantification of F. prausnitzii in stool prior to antibiotic administration may help identify patients at risk of AAD, and aid clinicians in devising individualized treatment regimens to minimize such adverse effects.

SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.

A comparative study of flow cytometry-sorted communities and shotgun viral metagenomics in a Singapore municipal wastewater treatment plant.

Traditional or "bulk" viral enrichment and amplification methods used in viral metagenomics introduce unavoidable bias in viral diversity. This bias is due to shortcomings in existing viral enrichment methods and overshadowing by the more abundant viral populations. To reduce the complexity and improve the resolution of viral diversity, we developed a strategy coupling fluorescence-activated cell sorting (FACS) with random amplification and compared this to bulk metagenomics. This strategy was validated on both influent and effluent samples from a municipal wastewater treatment plant using the Modified Ludzack-Ettinger (MLE) process as the treatment method. We found that DNA and RNA communities generated using bulk samples were mostly different from those derived following FACS for both treatments before and after MLE. Before MLE treatment, FACS identified five viral families and 512 viral annotated contigs. Up to 43% of mapped reads were not detected in bulk samples. Nucleo-cytoplasmic large DNA viral families were enriched to a greater extent in the FACS-coupled subpopulations compared with bulk samples. FACS-coupled viromes captured a single-contig viral genome associated with Anabaena phage, which was not observed in bulk samples or in FACS-sorted samples after MLE. These short metagenomic reads, which were assembled into a high-quality draft genome of 46 kbp, were found to be highly dominant in one of the pre-MLE FACS annotated virome fractions (57.4%). Using bulk metagenomics, we identified that between Primary Settling Tank and Secondary Settling Tank viromes, Virgaviridae, Astroviridae, Parvoviridae, Picobirnaviridae, Nodaviridae, and Iridoviridae were susceptible to MLE treatment. In all, bulk and FACS-coupled metagenomics are complementary approaches that enable a more thorough understanding of the community structure of DNA and RNA viruses in complex environmental samples, of which the latter is critical for increasing the sensitivity of detection of viral signatures that would otherwise be lost through bulk viral metagenomics.

Learning From Each Other in the Management of Natural Disaster and COVID-19 Pandemic: A Case Study in Taiwan.

In response to the COVID-19 pandemic, Taiwan has been one of the best performers in the world with extremely low infections and deaths. This success can be attributed to the long experiences dealing with natural disasters and communicable diseases. However, with different disastrous characteristics, the disaster management systems for communicable diseases and natural disasters are very different in terms of laws, plans, frameworks, and emergency operations. Taking the response to COVID-19 pandemic as a study subject, we found that disaster management for communicable diseases can be improved through a comparison with natural disasters, and vice versa. First, having wider and longer impacts than natural disasters, the plans and framework for communicable diseases in Taiwan focus more on national and regional scales. Local governments would need more capacity support including budgets and training to conduct investigations and quarantine during the COVID-19 pandemic. Second, for quick response, the emergency operation for communicable diseases was designed to be more flexible than that for natural disasters by giving the commander more authority to adjust to the circumstances. The commanding system requires a more objective consultation group to prevent arbitrary decisions against the COVID-19 pandemic. Finally, risk governance is important for communicable diseases as well as for natural disasters. Additional efforts should be made to enhance vulnerability assessment, disaster reduction, and risk communication for shaping responses and policies in an efficient and coordinating way.

Wastewater surveillance of SARS-CoV-2 across 40 U.S. states from February to June 2020.

Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics.

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater viral titers and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Wastewater Surveillance of SARS-CoV-2 across 40 U.S. states.

Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.

Experience from a Multi-Disciplinary Team Against COVID-19: A Healthcare Perspective.

Globally, the capacity of healthcare systems across continents has been strained and put to the test with the emergence of the Coronavirus disease 2019 (COVID-19) pandemic. The timely need to ensure the availability of healthcare facilities to isolate and manage the surge in COVID-19 cases without overwhelming existing hospital capacity has posed challenges in many countries. In this paper, we discuss the conceptualisation, preparations and operationalisation of a community healthcare facility that was set up within a short time frame to attend to the convalescent needs of a large number of COVID-19 patients in the early phase of handling the pandemic. In the first month of operations, we monitored a total of 2129 clinical encounters, with the majority of patients between 17-35 years of age and between day 2 to day 6 of illness upon admission. Overall, there was a good outcome for the patients, with only 2.3% requiring transfer back to restructured hospitals. There was also no mortality. We hope that the sharing of our experiences of the challenges and learning lessons gleaned may be useful to guide individuals in planning for the future preparedness of healthcare systems in managing pandemics.

SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.

SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases.

Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.

Making waves: Wastewater surveillance of SARS-CoV-2 for population-based health management.

Worldwide, clinical data remain the gold standard for disease surveillance and tracking. However, such data are limited due to factors such as reporting bias and inability to track asymptomatic disease carriers. Disease agents are excreted in the urine and feces of infected individuals regardless of disease symptom severity. Wastewater surveillance - that is, monitoring disease via human effluent - represents a valuable complement to clinical approaches. Because wastewater is relatively inexpensive and easy to collect and can be monitored at different levels of population aggregation as needed, wastewater surveillance can offer a real-time, cost-effective view of a community's health that is independent of biases associated with case-reporting. For SARS-CoV-2 and other disease-causing agents we envision an aggregate wastewater-monitoring system at the level of a wastewater treatment plant and exploratory or confirmatory monitoring of the sewerage system at the neighborhood scale to identify or confirm clusters of infection or assess impact of control measures where transmission has been established. Implementation will require constructing a framework with collaborating government agencies, public or private utilities, and civil society organizations for appropriate use of data collected from wastewater, identification of an appropriate scale of sample collection and aggregation to balance privacy concerns and risk of stigmatization with public health preservation, and consideration of the social implications of wastewater surveillance.