Wastewater-based epidemiology predicts COVID-19 community prevalence

Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology

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.

Wastewater-based estimation of diabetes mellitus prevalence in 237 cities: A cross-China study

Chapter 10 - Viral surveillance of wastewater as a promising tool to assess the spread of pathogens in the population: the experience of SARS-CoV-2

A quantitative decision-support framework for assessing the feasibility and sensitivity of wastewater-based epidemiology of respiratory virus surveillance.

Monitoring SARS-CoV-2 RNA in industrial wastewater has emerged as a potential approach to indirectly identify infected individuals within workplace populations. However, wastewater-based surveillance of SARS-CoV-2 in industrial settings has not previously been conducted in Thailand. This study aimed to qualitatively and quantitatively detect SARS-CoV-2 RNA in wastewater discharged from onsite wastewater treatment plants (WWTPs) and to estimate the number of infected individuals. A total of 120 wastewater samples were consecutively collected from 40 onsite WWTPs comprising 38 factories and two non-factory buildings with over a three-month period (April to June 2023), after the Songkran festival, Thailand's traditional new year celebration. Wastewater samples were concentrated, and the total nucleic acids were extracted. SARS-CoV-2 RNA concentrations were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) targeting the N gene. SARS-CoV-2 RNA was detected in 42.5%, 62.5%, and 40.0% of samples collected in April, May, and June, respectively. The concentrations ranged from 3.5 × 102-1.1 × 104 copies/L in April, 4 × 102-1.7 × 105 copies/L in May, and 1.1 × 103-1.4 × 105 copies/L in June. A higher frequency of SARS-CoV-2 RNA detection was observed in factories having more than 1000 employees. Based on our modified estimation model, the estimated infected individuals were 219, 3145, and 2711 for April, May, and June, respectively. The numbers of estimated infected population closely aligned with the official COVID-19 case trending reported in Pathum Thani province and at the national level during the same period. These findings suggest that wastewater-based epidemiology (WBE) is a reliable tool for measuring, estimating, and assessing COVID-19 dynamics in industrial setting.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 105 copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5–13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.

Canine distemper virus (CDV), canine parvovirus (CPV), and Streptococcus zooepidemicus (S. zoo) represent a range of infectious agents that can cause epidemic disease in dogs, particularly those housed in animal shelters. Outbreaks of disease associated with these agents are relatively common in shelters, resulting in isolation, quarantine, euthanasia, and in some cases, shelter closure in efforts to manage the outbreak. In recent years, outbreaks of CDV and S. zoo have been particularly challenging for shelters in Arizona. Wastewater-based epidemiology (WBE) has proven useful in disease monitoring and outbreak prediction in human populations, including congregate locations, and we hypothesized that it may also be useful in surveillance and prediction for agents responsible for either gastrointestinal (GI) or respiratory disease in shelter animals. Our specific aims were to develop and validate a protocol for detecting infectious agents from wastewater samples collected from shelter run sections and to investigate associations with agent burden and counts of animals with clinical signs. Once protocols for sampling and preparation were optimized, environmental wastewater samples were collected weekly between November 2023 and April 2024 at a large open intake municipal animal shelter in Phoenix, Arizona. The samples were collected from seven drains located in seven run sections in three separate buildings, and were evaluated for the presence of the three agent nucleic acids using quantitative, multiplex PCR. The counts of dogs actively showing GI and respiratory symptoms, graded as mild, moderate, and severe, were collected the afternoon of the day of sampling. Biosafety committee approval was granted for the viruses in this study. Shedding of CPV was found to be widespread and variable over time. S. zoo was detected five times on five different weeks in five different run sections. CDV was found once. There were only six dogs reported with GI signs during the study period. Counts of respiratory disease ranged from 42-132 dogs per run section with most cases being mild in severity and reported in the isolation run section. There is no apparent association between counts of either respiratory or GI cases and the three agents detected through wastewater sampling. During the study there was a single incident of a S. zoo PCR positive dog being isolated and later dying at the shelter. S. zoo was detected through wastewater sampling the week prior to the incident, however, in a run section located in a different building than where the PCR positive dog was housed. To the authors’ knowledge, this study is the first of its kind to develop protocols to detect critical pathogens in wastewater samples from an animal shelter. CPV has been the pathogen most frequently detected, which we speculate may be largely due to the shedding typically observed in dogs post-vaccination, given the absence of GI signs. Further investigation is warranted to better characterize the absence of amplifiable CDV nucleic acids and to examine these samples for other common shelter pathogens.

Urinary concentrations of bisphenol analogues in the south of China population and their contribution to the per capital mass loads in wastewater

Recovery of SARS-CoV-2 from large volumes of raw wastewater is enhanced with the inuvai R180 system

Kisspeptins are neuropeptides encoded by the Kiss1 gene, which have been implicated in the neuroendocrine regulation of gonadotropin-releasing hormone (GnRH) secretion. The goal of this study was to test the hypothesis that activation of Kiss1 neurons in the anteroventral periventricular nucleus (AVPV) is linked to the induction of the preovulatory luteinizing hormone (LH) surge in the rat. First, we determined that levels of Kiss1 mRNA in the AVPV peaked during the evening of proestrus, whereas Kiss1 mRNA in the arcuate nucleus (Arc) was at its nadir. Second, we corroborated this observation by demonstrating that Kiss1 mRNA is increased in the AVPV at the time of an estrogen (E)- and progesterone-induced LH surge in ovariectomized animals, whereas in the Arc, the expression of Kiss1 mRNA was decreased. Third, we found that most Kiss1 neurons in the AVPV coexpress the immediate early gene Fos coincidently with the LH surge, but virtually none coexpressed Fos on diestrus. In contrast, Kiss1 neurons in the Arc were Fos negative at the time of the LH surge as well as on diestrus. Finally, we found that most Kiss1 neurons in the AVPV and Arc express estrogen receptor alpha mRNA, suggesting that E acts directly on these neurons. These results suggest that Kiss1 neurons in the AVPV play an active role in mediating the effects of E on the generation of the preovulatory GnRH/LH surge on proestrus.

Wastewater-based epidemiology may be useful for informing public health response to viral diseases like COVID-19 caused by SARS-CoV-2. We quantified SARS-CoV-2 RNA in wastewater influent and primary settled solids in two wastewater treatment plants to inform the preanalytical and analytical approaches and to assess whether influent or solids harbored more viral targets. The primary settled solids samples resulted in higher SARS-CoV-2 detection frequencies than the corresponding influent samples. Likewise, SARS-CoV-2 RNA was more readily detected in solids using one-step digital droplet (dd)RT-PCR than with two-step RT-QPCR and two-step ddRT-PCR, likely owing to reduced inhibition with the one-step ddRT-PCR assay. We subsequently analyzed a longitudinal time series of 89 settled solids samples from a single plant for SARS-CoV-2 RNA as well as coronavirus recovery (bovine coronavirus) and fecal strength (pepper mild mottle virus) controls. SARS-CoV-2 RNA targets N1 and N2 concentrations correlated positively and significantly with COVID-19 clinically confirmed case counts in the sewershed. Together, the results demonstrate that measuring SARS-CoV-2 RNA concentrations in settled solids may be a more sensitive approach than measuring SARS-CoV-2 in influent.

Age-dependent association between SARS-CoV-2 cases reported by passive surveillance and viral load in wastewater

Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence – Insights from long-term surveillance