Wastewater-based epidemiology (WBE) uses concentrations of infectious agent targets in wastewater to infer infection trends in the contributing community. To date, WBE has been used to gain insight into infection trends of gastrointestinal diseases, but its application to respiratory diseases has been limited. Here, we report that respiratory syncytial virus (RSV) genomic ribonucleic acid can be detected in wastewater settled solids at two publicly owned treatment works. We further show that its concentration in settled solids is strongly associated (Kendalls tau = 0.65–0.77, p < 10–7) with clinical positivity rates for RSV at sentinel laboratories across the state in 2021, a year with anomalous seasonal trends of RSV disease. Given that RSV infections have similar clinical presentations to COVID-19, can be life threatening for some, and immunoprophylaxis distribution for vulnerable people is based on outbreak identification, WBE represents an important tool to augment current RSV surveillance and public health response efforts.
Data on community-acquired antibiotic-resistant bacterial infections are particularly sparse in low-and middleincome countries (LMICs). Limited surveillance and oversight of antibiotic use in food-producing animals, inadequate access to safe drinking water, and insufficient sanitation and hygiene infrastructure in LMICs could exacerbate the risk of zoonotic antibiotic resistance transmission. This critical review compiles evidence of zoonotic exchange of antibiotic-resistant bacteria (ARB) or antibiotic resistance genes (ARGs) within households and backyard farms in LMICs, as well as assesses transmission mechanisms, risk factors, and environmental transmission pathways. Overall, substantial evidence exists for exchange of antibiotic resistance between domesticated animals and in-contact humans. Whole bacteria transmission and horizontal gene transfer between humans and animals were demonstrated within and between households and backyard farms. Further, we identified water, soil, and animal food products as environmental transmission pathways for exchange of ARB and ARGs between animals and humans, although directionality of transmission is poorly understood. Herein we propose study designs, methods, and topical considerations for priority incorporation into future One Health research to inform effective interventions and policies to disrupt zoonotic antibiotic resistance exchange in low-income communities.
Wastewater based epidemiology (WBE) uses concentrations of infectious agent targets in wastewater to infer infection trends in the contributing community. To date, WBE has been used to gain insight into infection trends of gastrointestinal diseases, but its application to respiratory diseases has been limited to COVID-19. Here we report Respiratory Syncytial Virus (RSV) genomic RNA can be detected in wastewater settled solids at two publicly owned treatment works (POTWs). We further show that its concentration in settled solids is strongly associated with clinical positivity rates for RSV at sentinel laboratories across the state in 2021, a year with anomalous seasonal trends in RSV disease. Given that RSV infections have similar clinical presentations to COVID-19, can be life threatening for some, and immunoprophylaxis distribution for vulnerable people is based on outbreak identification, WBE represents an important tool to augment current RSV surveillance and public health response efforts.Graphical AbstractSynopsisRespiratory Syncytial Virus RNA concentrations in settled solids from wastewater treatment plants are associated with state-wide RSV clinical positivity rates.
SARS-CoV-2 RNA concentrations in wastewater solids and liquids are correlated with reported incident COVID-19 cases. Reporting of incident COVID-19 cases has changed dramatically with the availability of at-home antigen tests. Wastewater monitoring therefore represents an objective tool for continued monitoring of COVID-19 occurrence. One important use case for wastewater data is identifying when there are sustained changes or trends in SARS-CoV-2 RNA concentrations. Such information can be used to inform public health messaging, testing, and vaccine resources. However, there is limited research on best approaches for identifying trends in wastewater monitoring data. To fill this knowledge gap, we applied three trend analysis methods (relative strength index (RSI), percent change (PC), Mann-Kendall (MK) trend test) to daily measurements of SARS-CoV-2 RNA in wastewater solids from a wastewater treatment plant to characterize trends. Because daily measurements are not common for wastewater monitoring programs, we also conducted a downsampling analysis to determine the minimum sampling frequency necessary to capture the trends identified using the gold standard daily data. The PC and MK trend test appear to perform similarly and better than the RSI in terms of early warning signaling for increasing and decreasing trends, so we only considered the PC and MK trend test methods in the downsampling analysis. Using an acceptable sensitivity and specificity cutoff of 0.5, we found that a minimum of 4 samples/week and 5 samples/week is necessary to detect trends identified by daily sampling using the PC and MK trend test method, respectively. If a higher sensitivity and specificity is needed, then more samples per week would be needed. Public health officials can adopt these trend analysis approaches and sampling frequency recommendations to wastewater monitoring programs aimed at providing information on how incident COVID-19 cases are changing in the contributing communities.
SARS-CoV-2 RNA concentrations in wastewater solids and liquids are correlated with reported incident COVID-19 cases. Reporting of incident COVID-19 cases has changed dramatically with the availability of at-home antigen tests. Wastewater monitoring therefore represents an objective tool for continued monitoring of COVID-19 occurrence. One important use case for wastewater data is identifying when there are sustained changes or trends in SARS-CoV-2 RNA concentrations. Such information can be used to inform public health messaging, testing, and vaccine resources. However, there is limited research on best approaches for identifying trends in wastewater monitoring data. To fill this knowledge gap, we applied three trend analysis methods (relative strength index (RSI), percent change (PC), Mann-Kendall (MK) trend test) to daily measurements of SARS-CoV-2 RNA in wastewater solids from a wastewater treatment plant to characterize trends. Because daily measurements are not common for wastewater monitoring programs, we also conducted a downsampling analysis to determine the minimum sampling frequency necessary to capture the trends identified using the “gold standard” daily data. The PC and MK trend test appear to perform similarly and better than the RSI in terms of first detecting increasing and decreasing trends using a 14-day look-back period, so we only considered the PC and MK trend test methods in the downsampling analysis. Using an acceptable sensitivity and specificity cutoff of 0.5, we found that a minimum of 4 samples/week and 5 samples/week is necessary to detect trends identified by daily sampling using the PC and MK trend test method, respectively. If a higher sensitivity and specificity is needed, then more samples per week would be needed. Public health officials can adopt these trend analysis approaches and sampling frequency recommendations to wastewater monitoring programs aimed at providing information on how incident COVID-19 cases are changing in the contributing communities.
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