Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach.

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

High-Spatial-Resolution Methane Emissions Calculation Using TROPOMI Data by a Divergence Method

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km ×7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions.

Micronutrient perspective on COVID-19: Umbrella review and reanalysis of meta-analyses.

INTRODUCTION: Micronutrients are clinically important in managing COVID-19, and numerous studies have been conducted, but inconsistent findings exist. OBJECTIVE: To explore the association between micronutrients and COVID-19. METHODS: PubMed, Web of Science, Embase, Cochrane Library and Scopus for study search on July 30, 2022 and October 15, 2022. Literature selection, data extraction and quality assessment were performed in a double-blinded, group discussion format. Meta-analysis with overlapping associations were reconsolidated using random effects models, and narrative evidence was performed in tabular presentations. RESULTS: 57 reviews and 57 latest original studies were included. 21 reviews and 53 original studies were of moderate to high quality. Vitamin D, vitamin B, zinc, selenium, and ferritin levels differed between patients and healthy people. Vitamin D and zinc deficiencies increased COVID-19 infection by 0.97-fold/0.39-fold and 1.53-fold. Vitamin D deficiency increased severity 0.86-fold, while low vitamin B and selenium levels reduced severity. Vitamin D and calcium deficiencies increased ICU admission by 1.09 and 4.09-fold. Vitamin D deficiency increased mechanical ventilation by 0.4-fold. Vitamin D, zinc, and calcium deficiencies increased COVID-19 mortality by 0.53-fold, 0.46-fold, and 5.99-fold, respectively. CONCLUSION: The associations between vitamin D, zinc, and calcium deficiencies and adverse evolution of COVID-19 were positive, while the association between vitamin C and COVID-19 was insignificant.REGISTRATION: PROSPERO CRD42022353953.

Impacts of the COVID-19 Pandemic upon Chinese Positive Traits.

Will Chinese people change in terms of their character strengths when disasters strike? As far as the most recent COVID-19 pandemic is concerned, we provide an explorative answer from the impacts of positive traits included in the Values in Action Classification of Strengths upon Chinese people. We conducted a large-scale online survey from 1 January 2019 to 13 February 2020, with 12,878 respondents nationwide, covering all the administrative regions in China and all age intervals. The changes in the 24 character strengths before and during the pandemic were compared. Results revealed a significant increase in teamwork triggered by the pandemic among Chinese people. Fine-grained differences in demographic variables were also examined. Results showed that the COVID-19 pandemic significantly boosted teamwork for both males and females. Concerning age differences, only younger adults (18-25-year-old) showed a significant increase in teamwork. Besides this, it was also discovered that females always performed a higher teamwork tendency than males, and the elderly higher than the younger, regardless of the pandemic.

Impact of COVID-19 outbreak risk perception on willingness to consume products from restaurants: mediation effect of risk attitude

The COVID-19 outbreak caused huge losses for the catering industry. The outbreak's influence on consumers' risk perception and risk attitude was an important factor for these heavy losses. The aim of this study was to investigate the change in epidemic risk perception, risk attitude, and the consumers' willingness to consume products from restaurants during the spread of the COVID-19 epidemic. The study collected 502 questionnaires at the end of 2021, and structural analysis was conducted using SPSS 26.0 and AMOS 20.0 statistical programs. The results showed that consumers' awareness of the coronavirus pandemic (consumers' epidemic risk perception) had a significant positive effect on their decision-making behavior under uncertain conditions (risk attitude);consumers' decision-making behavior under uncertain conditions (risk attitude) had a significant negative effect on their willingness to purchase from restaurants;consumers' awareness of the coronavirus pandemic (consumers' epidemic risk perception) had a significant negative effect on their willingness to consume products from restaurants;and risk attitude played a mediating role in the influence of consumers' epidemic risk perception on their willingness to consume products from restaurants. This study can provide guidance and reference for restaurants on how to deal with the epidemic situation, help them undertake risk prevention work and reduce losses, and promote the healthy and sustainable development of the restaurant.

Simple methods for early warnings of COVID-19 surges: Lessons learned from 21 months of wastewater and clinical data collection in Detroit, Michigan, United States.

Wastewater-based epidemiology (WBE) has drawn great attention since the Coronavirus disease 2019 (COVID-19) pandemic, not only due to its capability to circumvent the limitations of traditional clinical surveillance, but also due to its potential to forewarn fluctuations of disease incidences in communities. One critical application of WBE is to provide "early warnings" for upcoming fluctuations of disease incidences in communities which traditional clinical testing is incapable to achieve. While intricate models have been developed to determine early warnings based on wastewater surveillance data, there is an exigent need for straightforward, rapid, broadly applicable methods for health departments and partner agencies to implement. Our purpose in this study is to develop and evaluate such early-warning methods and clinical-case peak-detection methods based on WBE data to mount an informed public health response. Throughout an extended wastewater surveillance period across Detroit, MI metropolitan area (the entire study period is from September 2020 to May 2022) we designed eight early-warning methods (three real-time and five post-factum). Additionally, we designed three peak-detection methods based on clinical epidemiological data. We demonstrated the utility of these methods for providing early warnings for COVID-19 incidences, with their counterpart accuracies evaluated by hit rates. "Hit rates" were defined as the number of early warning dates (using wastewater surveillance data) that captured defined peaks (using clinical epidemiological data) divided by the total number of early warning dates. Hit rates demonstrated that the accuracy of both real-time and post-factum methods could reach 100 %. Furthermore, the results indicate that the accuracy was influenced by approaches to defining peaks of disease incidence. The proposed methods herein can assist health departments capitalizing on WBE data to assess trends and implement quick public health responses to future epidemics. Besides, this study elucidated critical factors affecting early warnings based on WBE amid the COVID-19 pandemic.

Characterizing data patterns with core-periphery network modeling

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns;on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value;otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data “without pattern” through a network approach, contributing to the general solution of classification.

Investigating the microbial inactivation effect of low temperature high pressure carbon dioxide and its application in frozen prawn (Penaeus vannamei).

During the pandemic of coronavirus disease 2019, the fact that frozen foods can carry the relevant virus raises concerns about the microbial safety of cold-chain foods. As a non-thermal processing technology, high pressure carbon dioxide (HPCD) is a potential method to reduce microbial load on cold-chain foods. In this study, we explored the microbial inactivation of low temperature (5-10 °C) HPCD (LT-HPCD) and evaluated its effect on the quality of prawn during freeze-chilled and frozen storage. LT-HPCD treatment at 6.5 MPa and 10 °C for 15 min could effectively inactivate E. coli (99.45%) and S. aureus (94.6%) suspended in 0.85% NaCl, SARS-CoV-2 Spike pseudovirus (>99%) and human coronavirus 229E (hCoV-229E) (>1-log virus tilter reduction) suspended in DMEM medium. The inactivation effect of LT-HPCD was weakened but still significant when the microorganisms were inoculated on the surface of food or package. LT-HPCD treatment at 6.5 MPa and 10 °C for 15 min achieved about 60% inactivation of total aerobic count while could maintain frozen state and quality of prawn. Moreover, LT-HPCD treated prawn exhibited significant slower microbial proliferation and no occurrence of melanosis compared with the untreated samples during chilled storage. A comprehensive quality investigation indicated that LT-HPCD treatment could maintain the color, texture and sensory of prawn during chilled or frozen storage. Consequently, LT-HPCD could improve the microbial safety of frozen prawn while maintaining its original quality, and could be a potential method for food industry to improve the microbial safety of cold-chain foods.

Usage of Compartmental Models in Predicting COVID-19 Outbreaks.

Accurately predicting the spread of the SARS-CoV-2, the cause of the COVID-19 pandemic, is of great value for global regulatory authorities to overcome a number of challenges including medication shortage, outcome of vaccination, and control strategies planning. Modeling methods that are used to simulate and predict the spread of COVID-19 include compartmental model, structured metapopulations, agent-based networks, deep learning, and complex network, with compartmental modeling as one of the most widely used methods. Compartmental model has two noteworthy features, a flexible framework that allows users to easily customize the model structure and its high adaptivity that allows well-matured approaches (e.g., Bayesian inference and mixed-effects modeling) to improve parameter estimation. We retrospectively evaluated the prediction performances of the compartmental models on the CDC COVID-19 Mathematical Modeling webpage based on data collected between August 2020 and February 2021, and subsequently discussed in detail their corresponding model enhancement. Finally, we presented examples using the compartmental models to assist policymaking. By evaluating all models in parallel, we systemically evaluated the performance and evolution of using compartmental models for COVID-19 pandemic prediction. In summary, as a 100-year-old epidemic approach, the compartmental model presents a powerful tool that is extremely adaptive and can be readily customized and implemented to address new data or emerging needs during a pandemic.

Surveillance of SARS-CoV-2 in nine neighborhood sewersheds in Detroit Tri-County area, United States: Assessing per capita SARS-CoV-2 estimations and COVID-19 incidence.

Wastewater-based epidemiology (WBE) has been suggested as a useful tool to predict the emergence and investigate the extent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we screened appropriate population biomarkers for wastewater SARS-CoV-2 normalization and compared the normalized SARS-CoV-2 values across locations with different demographic characteristics in southeastern Michigan. Wastewater samples were collected between December 2020 and October 2021 from nine neighborhood sewersheds in the Detroit Tri-County area. Using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR), concentrations of N1 and N2 genes in the studied sites were quantified, with N1 values ranging from 1.92 × 102 genomic copies/L to 6.87 × 103 gc/L and N2 values ranging from 1.91 × 102 gc/L to 6.45 × 103 gc/L. The strongest correlations were observed with between cumulative COVID-19 cases per capita (referred as COVID-19 incidences thereafter), and SARS-CoV-2 concentrations normalized by total Kjeldahl nitrogen (TKN), creatinine, 5-hydroxyindoleacetic acid (5-HIAA) and xanthine when correlating the per capita SARS-CoV-2 and COVID-19 incidences. When SARS-CoV-2 concentrations in wastewater were normalized and compared with COVID-19 incidences, the differences between neighborhoods of varying demographics were reduced as compared to differences observed when comparing non-normalized SARS-CoV-2 with COVID-19 cases. This indicates when studying the disease burden in communities of different demographics, accurate per capita estimation is of great importance. The study suggests that monitoring selected water quality parameters or biomarkers, along with RNA concentrations in wastewater, will allow adequate data normalization for spatial comparisons, especially in areas where detailed sanitary sewage flows and contributing populations in the catchment areas are not available. This opens the possibility of using WBE to assess community infections in rural areas or the developing world where the contributing population of a sample could be unknown.

Allosteric aptasensor-initiated target cycling and transcription amplification of light-up RNA aptamer for sensitive detection of protein.

The early detection of biomarker proteins in clinical samples is of great significance for the diagnosis of diseases. However, it is still a challenge to detect low-concentration protein. Herein, a label-free aptamer-based amplification assay, termed the ATC-TA system, that allows fluorescence detection of very low numbers of protein without time-consuming washing steps and pre-treatment was developed. The target induces a conformational change in the allosteric aptasensor, triggers the target cycling and transcription amplification, and ultimately converts the input of the target protein into the output of the light-up aptamer (R-Pepper). It exhibits ultrahigh sensitivity with a detection limit of 5.62 fM at 37 â„ƒ and the accuracy is comparable to conventional ELISA. ATC-TA has potential application for the detection of endogenous PDGF-BB in serum samples to distinguish tumor mice from healthy mice at an early stage. It also successfully detects exogenous SARS-CoV-2 spike proteins in human serum. Therefore, this high-sensitive, universality, easy-to-operate and cost-effective biosensing platform holds great clinical application potential in early clinical diagnosis.

Internet of Things use case applications for COVID-19

The Internet of Things (IoT) is a technology built upon various physical objects equipped with different types of sensors, which are connected together using communication methods. These devices have been applied to several domains, especially healthcare. In addition to the numerous benefits that IoT has demonstrated in healthcare, this technology is being adopted for combating the recent COVID-19 pandemic. The key role of IoT in COVID-19 could be classified into five major tasks: Monitoring, Diagnosing, Tracing, Disinfecting, and Vaccinating. This chapter reviews the state-of-art applications of IoT based on these tasks in order to better mitigate this virus. Additionally, potential areas for applying IoT systems to fight against COVID-19 or even future pandemics will be demonstrated.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance.

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.

Signaling Through FcγRIIA and the C5a-C5aR Pathway Mediate Platelet Hyperactivation in COVID-19.

Patients with COVID-19 present with a wide variety of clinical manifestations. Thromboembolic events constitute a significant cause of morbidity and mortality in patients infected with SARS-CoV-2. Severe COVID-19 has been associated with hyperinflammation and pre-existing cardiovascular disease. Platelets are important mediators and sensors of inflammation and are directly affected by cardiovascular stressors. In this report, we found that platelets from severely ill, hospitalized COVID-19 patients exhibited higher basal levels of activation measured by P-selectin surface expression and had poor functional reserve upon in vitro stimulation. To investigate this question in more detail, we developed an assay to assess the capacity of plasma from COVID-19 patients to activate platelets from healthy donors. Platelet activation was a common feature of plasma from COVID-19 patients and correlated with key measures of clinical outcome including kidney and liver injury, and APACHEIII scores. Further, we identified ferritin as a pivotal clinical marker associated with platelet hyperactivation. The COVID-19 plasma-mediated effect on control platelets was highest for patients that subsequently developed inpatient thrombotic events. Proteomic analysis of plasma from COVID-19 patients identified key mediators of inflammation and cardiovascular disease that positively correlated with in vitro platelet activation. Mechanistically, blocking the signaling of the FcγRIIa-Syk and C5a-C5aR pathways on platelets, using antibody-mediated neutralization, IgG depletion or the Syk inhibitor fostamatinib, reversed this hyperactivity driven by COVID-19 plasma and prevented platelet aggregation in endothelial microfluidic chamber conditions. These data identified these potentially actionable pathways as central for platelet activation and/or vascular complications and clinical outcomes in COVID-19 patients. In conclusion, we reveal a key role of platelet-mediated immunothrombosis in COVID-19 and identify distinct, clinically relevant, targetable signaling pathways that mediate this effect.

Quantitative methods and modeling to assess COVID-19-interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches.

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a "batch" effect to the mixed effects model may be used when a significant "batch" effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

Impact of Home-Based Learning Experience During COVID-19 on Future Intentions to Study Online: A Chinese University Perspective.

As coronavirus disease 2019 (COVID-19) swept the world in early 2020, all the Chinese universities and colleges adopted online learning to fulfill the directive saying "classes suspended but learning continues." Understanding the impact of this large-scale online learning experience on the future online learning intention of Chinese university students can help design better blended-learning activities. This study applies flow experience and theory of planned behavior (TPB) to construct a theoretical framework for assumption making and the assumptions made are validated by data gained from questionnaires. A total of 6,933 students from 54 institutions in China participated in the investigation, with 5,456 valid questionnaires returned. This study employs partial least squares (PLS) regression and confirmative factor analysis (CFA) to analyze and estimate the measurement model and the structural model. The results indicate that the experience of home-based learning significantly influenced the attitudes of Chinese university students, which in turn had a positive influence on their intention to continue online learning. The research findings provide a theoretical framework and practical guidelines on building a scientific online learning platform with appropriate online learning environments and tasks for a post-COVID-19 era blended-learning in Chinese universities.

Evidence Mapping of 23 Systematic Reviews of Traditional Chinese Medicine Combined With Western Medicine Approaches for COVID-19

Background: Coronavirus disease 2019 (COVID-19) has already spread around the world. The modality of traditional Chinese medicine (TCM) combined with Western medicine (WM) approaches is being used to treat COVID-19 patients in China. Several systematic reviews (SRs) are available highlighting the efficacy and safety of TCM combined with WM approaches in COVID-19 patients. However, their evidence quality is not completely validated. Purpose: We aimed to assess the methodological quality and the risk of bias of the included SRs, assess the evidence quality of outcomes, and present their trends and gaps using the evidence mapping method. Methods: PubMed, Cochrane Library, Embase, CNKI, CBM, and Wanfang Data were searched from inception until March 2021 to identify SRs pertaining to the field of TCM combined with WM approaches for COVID-19. The methodological quality of the SRs was assessed using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2), the risk of bias of the included SRs was assessed with the Risk of Bias in Systematic Review (ROBIS) tool, and the evidence quality of outcomes was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Results: In total, 23 SRs were found eligible. Twenty-one were rated of moderate confidence by AMSTAR 2, while 12 were rated at low risk using the ROBIS tool. In addition, most outcomes were graded as having moderate quality using the GRADE system. We found that the combined use of TCM and WM approaches could improve the CT recovery rate, effective rate, viral nucleic acid negative conversion rate, and the disappearance rate of fever, cough, and shortness of breath. Also, these approaches could decrease the conversion rate from mild to critical, white blood cell counts, and lymphocyte counts and shorten the time to viral assay conversion and the length of hospital stay. Conclusion: TCM combined with WM approaches had advantages in efficacy, laboratory, and clinical symptom outcomes of COVID-19, but the methodological deficiencies of SRs should be taken into consideration. Therefore, to better guide clinical practice in the future, the methodological quality of SRs should still be improved, and high-quality randomized controlled trials (RCTs) and observational studies should also be carried out.

Early Warnings of COVID-19 Second Wave in Detroit

This study focuses on using wastewater-based epidemiology to provide early warnings of the second COVID-19 wave in the Detroit metropolitan area of Michigan. SARS-CoV-2 RNA from untreated wastewater samples was compared to reported public health records. Untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF), located in southeast Michigan, between August 6, 2020 and December 14, 2020. The WRRF receives wastewater from its service area via three main interceptors: the Detroit River Interceptor (DRI), the North Interceptor-East Arm (NIEA), and the Oakwood-Northwest-Wayne County Interceptor (ONWI). A total of 144 untreated wastewater samples were collected (45, 48, and 51 for ONWI, NIEA, and DRI, respectively) at the point of intake into the WRRF. Virus-selective sampling was conducted, and viruses were isolated from wastewater using electropositive NanoCeram column filters. For each sample, an average of 33 L of wastewater was passed through NanoCeram electropositive cartridge filters at an average rate of 11  L/min. Viruses were eluted and concentrated, and the SARS-CoV-2 RNA concentrations were quantified with RT-qPCR. SARS-CoV-2 RNA was detected in 98% of the samples, and measured concentrations were in the range of 4.45×104 to 5.30×106 genomic copies/L. Early warnings of COVID-19 peaks were observed approximately 4 weeks prior to reported publicly available clinical data.