Wastewater-Based Epidemiology for Managing the COVID-19 Pandemic
Ontology highlight
ABSTRACT:
SUBMITTER: Fuschi C
PROVIDER: S-EPMC8130627 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The main objective of this study is to offer and evaluate an interim triage approach for patients waiting for surveillance colonoscopies. This will reduce the waiting period and the psychological stressors for our patients and from a scientific point of view allow us to compare the yield of findings for each approach.
| 2383510 | ecrin-mdr-crc
Project description:The outbreak of coronavirus disease (COVID-19) has caused a significant public health challenge worldwide. A lack of effective methods for screening potential patients, rapidly diagnosing suspected cases, and accurately monitoring of the epidemic in real time to prevent the rapid spread of COVID-19 raises significant difficulties in mitigating the epidemic in many countries. As effective point-of-care diagnosis tools, simple, low-cost and rapid sensors have the potential to greatly accelerate the screening and diagnosis of suspected patients to improve their treatment and care. In particular, there is evidence that multiple pathogens have been detected in sewage, including SARS-CoV-2, providing significant opportunities for the development of advanced sensors for wastewater-based epidemiology that provide an early warning of the pandemic within the population. Sensors could be used to screen potential carriers, provide real-time monitoring and control of the epidemic, and even support targeted drug screening and delivery within the integration of emerging mobile health (mHealth) technology. In this communication, we discuss the feasibility of an integrated point-of-care biosensor system with mobile health for wastewater-based epidemiology (iBMW) for early warning of COVID-19, screening and diagnosis of potential infectors, and improving health care and public health. The iBMW will provide an effective approach to prevent, evaluate and intervene in a fast, affordable and reliable way, thus enabling real-time guidance for the government in providing effective intervention and evaluating the effectiveness of intervention. Highlights • Point-of-care biosensors for rapid and reliable COVID-19 diagnosis.• Community sewage sensors for tracing and early warning of COVID-19 within population.• Point-of-use sensor-based mHealth for health care and epidemic management and intervention.• Microfluidics for effective drug delivery for potential therapy of COVID-19.
| S-EPMC7492834 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) has been used to analyze markers in wastewater treatment plant (WWTP) influent to characterize emerging chemicals, drug use patterns, or disease spread within communities. This approach can be particularly helpful in understanding outbreaks of disease like the novel Coronavirus disease-19 (COVID-19) when combined with clinical datasets. In this study, three RT-ddPCR assays (N1, N2, N3) were used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in weekly samples from nine WWTPs in southeastern Virginia. In the first several weeks of sampling, SARS-CoV-2 detections were sporadic. Frequency of detections and overall concentrations of RNA within samples increased from mid March into late July. During the twenty-one week study, SARS-CoV-2 concentrations ranged from 101 to 104 copies 100 mL-1 in samples where viral RNA was detected. Fluctuations in population normalized loading rates in several of the WWTP service areas agreed with known outbreaks during the study. Here we propose several ways that data can be presented spatially and temporally to be of greatest use to public health officials. As the COVID-19 pandemic wanes, it is likely that communities will see increased incidence of small, localized outbreaks. In these instances, WBE could be used as a pre-screening tool to better target clinical testing needs in communities with limited resources.
| S-EPMC7424388 | biostudies-literature
Project description:The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater produced interest in its use for sentinel surveillance at a community level and as a complementary approach to syndromic surveillance. With this work, we set the foundations for wastewater-based epidemiology (WBE) in Portugal by monitoring the trends of SARS-CoV-2 RNA circulation in the community, on a nationwide perspective during different epidemiological phases of the pandemic. The Charité assays (E_Sarbecco, RdRP, and N_Sarbecco) were applied to monitor, over 32-weeks (April to December 2020), the dynamics of SARS-CoV-2 RNA at the inlet of five wastewater treatment plants (WWTP), which together serve more than two million people in Portugal. Raw wastewater from three Coronavirus disease 2019 (COVID-19) reference hospitals was also analyzed during this period. In total, more than 600 samples were tested. For the first weeks, detection of SARS-CoV-2 RNA was sporadic, with concentrations varying from 103 to 105 genome copies per liter (GC/L). Prevalence of SARS-CoV-2 RNA increased steeply by the end of May into late June, mainly in Lisboa e Vale do Tejo region (LVT), during the reopening phase. After the summer, with the reopening of schools in mid-September and return to partial face-to-face work, a pronounced increase of SARS-CoV-2 RNA in wastewater was detected. In the LVT area, SARS-CoV-2 RNA load agreed with reported trends in hotspots of infection. Synchrony between trends of SARS-CoV-2 RNA in raw wastewater and daily new COVID-19 cases highlights the value of WBE as a surveillance tool, particularly after the phasing out of the epidemiological curve and when hotspots of disease re-emerge in the population which might be difficult to spot based solely on syndromic surveillance and contact tracing. This is the first study crossing several epidemiological stages highlighting the long-term use of WBE for SARS-CoV-2.
| S-EPMC8432975 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.
| S-EPMC8141262 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) pandemic has affected almost every country in the world and has changed the way we access healthcare. People with pre-existing conditions, such as diabetes, are at high risk of a severe disease course and it is essential that, as well as good hygiene and social distancing measures, blood glucose is carefully monitored, as chronic hyperglycaemia can lead to immune dysfunction. People with diabetes should be encouraged to continue medication prescribed for hypertension, diabetes or dyslipidaemia. Furthermore, patients with diabetes and COVID-19 infection should follow their usual antidiabetic treatment with the exception of sodium-glucose cotransporter-2 inhibitors. As the current pandemic situation has rendered some patients unable to access routine healthcare, telehealth may help those with travel restrictions.
| S-EPMC7572158 | biostudies-literature
Project description:Raw municipal wastewater from five wastewater treatment plants representing the vast majority of the Qatar population was sampled between the third week of June 2020 and the end of August 2020, during the period of declining cases after the peak of the first wave of infection in May 2020. The N1 region of the SARS-CoV-2 genome was used to quantify the viral load in the wastewater using RT-qPCR. The trend in Ct values in the wastewater samples mirrored the number of new daily positive cases officially reported for the country, confirmed by RT-qPCR testing of naso-pharyngeal swabs. SARS-CoV-2 RNA was detected in 100% of the influent wastewater samples (7889 ± 1421 copy/L - 542,056 ± 25,775 copy/L, based on the N1 assay). A mathematical model for wastewater-based epidemiology was developed and used to estimate the number of people in the population infected with COVID-19 from the N1 Ct values in the wastewater samples. The estimated number of infected population on any given day using the wastewater-based epidemiology approach declined from 542,313 ± 51,159 to 31,181 ± 3081 over the course of the sampling period, which was significantly higher than the officially reported numbers. However, seroprevalence data from Qatar indicates that diagnosed infections represented only about 10% of actual cases. The model estimates were lower than the corrected numbers based on application of a static diagnosis ratio of 10% to the RT-qPCR identified cases, which is assumed to be due to the difficulty in quantifying RNA losses as a model term. However, these results indicate that the presented WBE modeling approach allows for a realistic assessment of incidence trend in a given population, with a more reliable estimation of the number of infected people at any given point in time than can be achieved using human biomonitoring alone.
| S-EPMC7870436 | biostudies-literature
Project description:Since 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), impaired public health with considerable morbidity and mortality due to the lack of vaccines and effective treatment. The severe disease mainly harmed adults with predisposing medical comorbidities (such as heart disease, hypertension, chronic lung disease), while it can occur in healthy individuals that may be asymptomatic. Wastewater-based Epidemiology (WBE), a non-invasive, objective, chemical tool was used to monitor and estimate the changes in drug's consumption and prescription patterns under normal conditions (2019) and under COVID-19 pandemic conditions (2020). NSAIDs, antihypertensives, diuretics, antiepileptics, antilipidemics, antibiotics, analgesics, antivirals, anticancer drugs, contrast iodinated drugs, antidiabetics, antiallergic drugs, antiulcers and other pharmaceuticals were studied in wastewater and revealed the application of various treatments during the first wave of the pandemic in Athens, Greece. Data were correlated with COVID-19 infection therapeutical plans. The result of the analysis revealed a remarkable increase for antiviral drugs (170%), hydroxychloroquine (387%), and antibiotics (57%), which were the most applied treatments against COVID-19 during the first wave in Greece. In agreement with related authorities urge, NSAIDs presented decrease (27%) during the first lockdown, while paracetamol demonstrated a remarkable increase (198%). The use levels for Angiotensin II receptor blockers such as valsartan, and co-administrated diuretics, such as hydrochlorothiazide, were reduced during 2020, by 32% and 26% respectively.
| S-EPMC8294694 | biostudies-literature
Project description:Since the COVID-19 pandemic, a decrease in the prevalence of Influenza A virus (IAV) and respiratory syncytial virus (RSV) has been suggested by clinical surveillance. However, there may be potential biases in obtaining an accurate overview of infectious diseases in a community. To elucidate the impact of the COVID-19 on the prevalence of IAV and RSV, we quantified IAV and RSV RNA in wastewater collected from three wastewater treatment plants (WWTPs) in Sapporo, Japan, between October 2018 and January 2023, using highly sensitive EPISENS™ method. From October 2018 to April 2020, the IAV M gene concentrations were positively correlated with the confirmed cases in the corresponding area (Spearman's r = 0.61). Subtype-specific HA genes of IAV were also detected, and their concentrations showed trends that were consistent with clinically reported cases. RSV A and B serotypes were also detected in wastewater, and their concentrations were positively correlated with the confirmed clinical cases (Spearman's r = 0.36-0.52). The detection ratios of IAV and RSV in wastewater decreased from 66.7 % (22/33) and 42.4 % (14/33) to 4.56 % (12/263) and 32.7 % (86/263), respectively in the city after the COVID-19 prevalence. The present study demonstrates the potential usefulness of wastewater-based epidemiology combined with the preservation of wastewater (wastewater banking) as a tool for better management of respiratory viral diseases.
| S-EPMC9991320 | biostudies-literature
Project description:Early detection and surveillance of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) virus are key pre-requisites for the effective control of coronavirus disease (COVID-19). So far, sewage testing has been increasingly employed as an alternative surveillance tool for this disease. However, sampling site characteristics impact the testing results and should be addressed in the early use stage of this emerging tool. In this study, we implemented the sewage testing for SARS-CoV-2 virus across sampling sites with different sewage system characteristics. We first validated a testing method using "positive" samples from a hospital treating COVID-19 patients. This method was used to test 107 sewage samples collected during the third wave of the COVID-19 outbreak in Hong Kong (from June 8 to September 29, 2020), covering sampling sites associated with a COVID-19 hospital, public housing estates, and conventional sewage treatment facilities. The highest viral titer of 1975 copy/mL in sewage was observed in a sample collected from the isolation ward of the COVID-19 hospital. Sewage sampling at individual buildings detected the virus 2 days before the first cases were identified. Sequencing of the detected viral fragment confirmed an identical nucleotide sequence to that of the SARS-CoV-2 isolated from human samples. The virus was also detected in sewage treatment facilities, which serve populations of approximately 40,000 to more than one million people.
| S-EPMC8142803 | biostudies-literature