Abstract:Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts… Show more
“…Seven additional articles were found through the citation search, giving a total of 43 articles that met the inclusion criteria ( Fig 1 ) ( Ahmed et al., 2023a ; Ahmed et al., 2023b ; Ahrens et al., 2023 ; Ando et al., 2023 ; Asadi et al., 2023 ; Bo et al., 2021 ; Boehm et al., 2023a ; Boehm et al., 2023b ; Borchardt et al., 2017 ; de Melo et al. 2023 ; Dumke et al., 2022 ; Germeraad et al., 2020 ; Hayes et al., 2023 ; He et al., 2017 ; Heijnen and Medema 2011a ; Henaux et al., 2012 ; Hinshaw et al., 1979 ; Horm et al., 2012 ; Horm et al., 2013 ; Ito et al., 1995 ; Khalil et al., 2022 ; Lai and McPhillips 1999 ; Leung et al., 2007 ; Li et al., 2022 ; Markt et al., 2023 ; Markwell and Shortridge 1982 ; Mercier et al., 2022 ; Munoz-Aguayo et al., 2019 ; Okuya et al., 2015 ; Okuya et al., 2023 ; Ornelas-Eusebio et al., 2015 ; Pawar et al., 2019 ; Rector et al., 2022 ; Rothman et al., 2021 ; Toribio-Avedillo et al., 2023 ; Vo et al., 2023 ; Vong et al., 2008 ; Wolfe et al., 2022 ; Wolken et al., 2023 ; Zafeiriadou et al., 2023 ; Zhang et al., 2011 ; Zhang et al., 2022 ; Zhang et al., 2012 ). …”
“…Seven additional articles were found through the citation search, giving a total of 43 articles that met the inclusion criteria ( Fig 1 ) ( Ahmed et al., 2023a ; Ahmed et al., 2023b ; Ahrens et al., 2023 ; Ando et al., 2023 ; Asadi et al., 2023 ; Bo et al., 2021 ; Boehm et al., 2023a ; Boehm et al., 2023b ; Borchardt et al., 2017 ; de Melo et al. 2023 ; Dumke et al., 2022 ; Germeraad et al., 2020 ; Hayes et al., 2023 ; He et al., 2017 ; Heijnen and Medema 2011a ; Henaux et al., 2012 ; Hinshaw et al., 1979 ; Horm et al., 2012 ; Horm et al., 2013 ; Ito et al., 1995 ; Khalil et al., 2022 ; Lai and McPhillips 1999 ; Leung et al., 2007 ; Li et al., 2022 ; Markt et al., 2023 ; Markwell and Shortridge 1982 ; Mercier et al., 2022 ; Munoz-Aguayo et al., 2019 ; Okuya et al., 2015 ; Okuya et al., 2023 ; Ornelas-Eusebio et al., 2015 ; Pawar et al., 2019 ; Rector et al., 2022 ; Rothman et al., 2021 ; Toribio-Avedillo et al., 2023 ; Vo et al., 2023 ; Vong et al., 2008 ; Wolfe et al., 2022 ; Wolken et al., 2023 ; Zafeiriadou et al., 2023 ; Zhang et al., 2011 ; Zhang et al., 2022 ; Zhang et al., 2012 ). …”
“…While this study has a focus on SARS-CoV-2 we also wish to emphasize its relevance for other viral diseases, e.g., Noro- or Influenzavirus 64 . Early warnings and epidemiological predictions based on sound models also for these viruses and others may help in local, regional or national prevention.…”
Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation—rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.
“…As SARS-CoV-2 surveillance in wastewater reaches scale [7,25,43], detection and quantification of other pathogens has been proposed. Researchers have expanded on wastewater monitoring to focus increased surveillance on other respiratory viruses such as human influenza and rhinovirus [44], norovirus [45], or as an outbreak detection tool for influenza, [46] and are also considering other emerging infections such as monkeypox. [47] .…”
Section: (Which Was Not Certified By Peer Review)mentioning
Wastewater-based epidemiology has emerged as a critical tool for public health surveillance, building on decades of environmental surveillance work for pathogens such as poliovirus. Work to date has been limited to monitoring a single pathogen or small numbers of pathogens in targeted studies; however, simultaneous analysis of a wide variety of pathogens would greatly increase the utility of wastewater surveillance. We developed a novel quantitative multi-pathogen surveillance approach (33 pathogen targets including bacteria, viruses, protozoa, and helminths) using TaqMan Array Cards (RT-qPCR) and applied the method on concentrated wastewater samples collected at four wastewater treatment plants in Atlanta, GA from February to October of 2020. From sewersheds serving approximately 2 million people, we detected a wide range of targets including many we expected to find in wastewater (e.g., enterotoxigenic E. coli and Giardia in 97% of 29 samples at stable concentrations) as well as unexpected targets including Strongyloides stercolaris (i.e., human threadworm, a neglected tropical disease rarely observed in clinical settings in the USA). Other notable detections included SARS-CoV-2, but also several pathogen targets that are not commonly included in wastewater surveillance like Acanthamoeba spp., Balantidium coli, Entamoeba histolytica, astrovirus, norovirus, and sapovirus. Our data suggest broad utility in expanding the scope of enteric pathogen surveillance in wastewaters, with potential for application in a variety of settings where pathogen quantification in fecal waste streams can inform public health surveillance and selection of control measures to limit infections.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.