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Environ Sci Technol ; 2022 Aug 02.
Article in English | MEDLINE | ID: covidwho-1972504


Since the COVID-19 pandemic started, there has been much speculation about how COVID-19 and antimicrobial resistance may be interconnected. In this study, untreated wastewater was sampled from Hospital A designated to treat COVID-19 patients during the first wave of the COVID-19 pandemic alongside Hospital B that did not receive any COVID-19 patients. Metagenomics was used to determine the relative abundance and mobile potential of antibiotic resistant genes (ARGs), prior to determining the correlation of ARGs with time/incidence of COVID-19. Our findings showed that ARGs resistant to macrolides, sulfonamides, and tetracyclines were positively correlated with time in Hospital A but not in Hospital B. Likewise, minor extended spectrum beta-lactamases (ESBLs) and carbapenemases of classes B and D were positively correlated with time, suggesting the selection of rare and/or carbapenem-resistant genes in Hospital A. Non-carbapenemase blaVEB also positively correlated with both time and intI1 and was copresent with other ARGs including carbapenem-resistant genes in 6 metagenome-assembled genomes (MAGs). This study highlighted concerns related to the dissemination of antimicrobial resistance (AMR) during the COVID-19 pandemic that may arise from antibiotic use and untreated hospital wastewater.

Preprint in English | EuropePMC | ID: ppcovidwho-327689


Background: Recent applications of wastewater-based epidemiology (WBE) have demonstrated its ability to track the spread and dynamics of COVID-19 at the community level. Despite the growing body of research, quantitative synthesis of SARS-CoV-2 titers in wastewater generated from studies across space and time using diverse methods has not been performed. Objective: The objective of this study is to examine the correlations between SARS-CoV-2 viral titers in wastewater across studies, stratified by key covariates in study methodologies. In addition, we examined the associations of proportions of positive detections (PPD) in wastewater samples and methodological covariates. Methods: We systematically searched the Web of Science for studies published by February 16th, 2021, performed a reproducible screen, and employed mixed-effects models to estimate the levels of SARS-CoV-2 viral titers in wastewater samples and their correlations to case prevalence, sampling mode (grab or composite sampling), and the fraction of analysis (FOA, i.e., solids, solid-supernatant mixtures, or supernatants/filtrates) Results: A hundred and one studies were found;twenty studies (1,877 observations) were retained following a reproducible screen. The mean of PPD across all studies was 0.67 (95%-CI, [0.56, 0.79]). The mean titer was 5,244.37 copies/mL (95%-CI, [0;16,432.65]). The Pearson Correlation coefficients (PCC) between viral titers and case prevalences were 0.28 (95%-CI, [0.01;0.51) for daily new cases or 0.29 (95%-CI, [-0.15;0.73]) for cumulative cases. FOA accounted for 12.4% of the variability in PPD, followed by case prevalence (9.3% by daily new cases and 5.9% by cumulative cases) and sampling mode (0.6%). Among observations with positive detections, FOA accounted for 56.0% of the variability in titers, followed by sampling mode (6.9%) and case prevalence (0.9% by daily new cases and 0.8% by cumulative cases). While sampling mode and FOA both significantly correlated with SARS-CoV-2 titers, the magnitudes of increase in PPD associated with FOA were larger. Mixed-effects model treating studies as random effects and case prevalence as fixed effects accounted for over 90% of the variability in SARS-CoV-2 PPD and titers. Interpretations: Positive pooled means and confidence intervals in PCC between SARS-CoV-2 titers and case prevalence indicators provide quantitative evidence reinforcing the value of wastewater-based monitoring of COVID-19. Large heterogeneities among studies in proportions of positive detections, titers, and PCC suggest a strong demand in methods to generate data accounting for cross-study heterogeneities and more detailed metadata reporting. Large variance explained by FOA suggesting FOA as a direction that needs to be prioritized in method standardization. Mixed-effects models accounting for study level variations provide a new perspective to synthesize data from multiple studies.

Environ Res ; 195: 110748, 2021 04.
Article in English | MEDLINE | ID: covidwho-1033702


There is increasing interest in wastewater-based epidemiology (WBE) of SARS-CoV-2 RNA to serve as an early warning system for a community. Despite successful detection of SARS-CoV-2 RNA in wastewaters sampled from multiple locations, there is still no clear idea on the minimal number of cases in a community that are associated with a positive detection of the virus in wastewater. To address this knowledge gap, we sampled wastewaters from a septic tank (n = 57) and biological activated sludge tank (n = 52) located on-site of a hospital. The hospital is providing treatment for SARS-CoV-2 infected patients, with the number of hospitalized patients per day known. It was observed that depending on which nucleocapsid gene is targeted by means of RT-qPCR, a range of 253-409 positive cases out of 10,000 persons are required prior to detecting RNA SARS-CoV-2 in wastewater. There was a weak correlation between N1 and N2 gene abundances in wastewater with the number of hospitalized cases. This correlation was however not observed for N3 gene. The frequency of detecting N1 and N2 gene in wastewater was also higher than that for N3 gene. Furthermore, nucleocapsid genes of SARS-CoV-2 were detected at lower frequency in the partially treated wastewater than in the septic tank. In particular, N1 gene abundance was associated with water quality parameters such as total organic carbon and pH. In instances of positive detection, the average abundance of N1 and N3 genes in the activated sludge tank were reduced by 50 and 70% of the levels detected in septic tank, suggesting degradation of the SARS-CoV-2 gene fragments already occurring in the early stages of the wastewater treatment process.

COVID-19 , SARS-CoV-2 , Disease Outbreaks , Humans , RNA, Viral/genetics , Waste Water