An improved method for determining frequency of multiple variants of SARS-CoV-2 in wastewater using qPCR assays.

Wastewater-based surveillance has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription polymerase chain reaction (RT-PCR) or whole genome sequencing (WGS). Rapid, reliable RT-PCR assays continue to be needed to determine the relative frequencies of VOCs and sub-lineages in wastewater-based surveillance programs. The presence of multiple mutations in a single region of the N-gene allowed for the design of a single amplicon, multiple probe assay, that can distinguish among several VOCs in wastewater RNA extracts. This approach which multiplexes probes designed to target mutations associated with specific VOC's along with an intra-amplicon universal probe (non-mutated region) was validated in singleplex and multiplex. The prevalence of each mutation (i.e. VOC) is estimated by comparing the abundance of the targeted mutation with a non-mutated and highly conserved region within the same amplicon. This is advantageous for the accurate and rapid estimation of variant frequencies in wastewater. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from several communities in Ontario, Canada in near real time from November 28, 2021 to January 4, 2022. This includes the period of the rapid replacement of the Delta variant with the introduction of the Omicron variant in these Ontario communities in early December 2021. The frequency estimates using this assay were highly reflective of clinical WGS estimates for the same communities. This style of qPCR assay, which simultaneously measures signal from a non-mutated comparator probe and multiple mutation-specific probes contained within a single qPCR amplicon, can be applied to future assay development for rapid and accurate estimations of variant frequencies.

A new ridge estimator for linear regression model with some challenging behavior of error term

Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter (Formula presented.) has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India. © 2023 Taylor & Francis Group, LLC.

Realizing the value in "non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard” data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology.

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

SARS-CoV-2 Immunoglobulin g (IgG) kinetics in healthcare workers and their close contacts reduced risk of re-infection

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for global pandemic, and it has caused more than 2.3 million deaths. Persistence and stability of immunoglobulin G (IgG) response after recovery from COVID-19 infection is still uncertain. Method(s): We performed a longitudinal cohort study in healthcare workers (HCW) and their close contacts (Non-HCW) with known resolved SARS-CoV-2 infection and undiagnosed infection at Maulana Azad Medical College and associated Lok Nayak hospital, New Delhi. Baseline IgG antibody titers was determined and the participants were followed over a period of six months. We also examined relationship between SARS-CoV-2 immunoglobulin G (IgG) response and new symptomatic infection in HCW and Non-HCW over time. Result(s): 176 (70.9%) healthcare workers and 72 (29.0%) non-healthcare workers were recruited from two cohorts. 82 subjects recovered from SARS-CoV-2 infection and 166 undiagnosed for the infection having history of close contact with COVID-19 patients were followed up for a median of 227 days (interquartile range, 166 to 202) after a positive IgG antibody test. In the recovered subjects 70.7% (58) were seropositive for first anti-spike IgG assay at baseline, followed by 80.0%, 90.6% and 82.6% at three visits respectively. In undiagnosed subjects 37.3% (62) were seropositive at baseline, followed by 70.9%, 75.8% and 82.2% respectively. Also, 46.8% (29) were asymptomatic with no symptoms of COVID-19 and were seropositive at baseline. However, presence of IgG antibodies was associated with substantial reduced risk of re-infection over the follow up duration. Conclusion(s): Our data showed that the antibodies levels measured increased over the first three months and decreased slightly after that and remained at a plateau and relatively stable for at least a period of six months. Copyright © 2022 International Medical Sciences Academy. All rights reserved.

Effect of COVID 19 pandemic on time to care, use of ambulance, admission characteristics, demography, injury characteristics, management and outcome of Paediatric Orthopaedic trauma patients admitted to the trauma centre

Background: Lockdown imposed to limit the spread of COVID 19 may have had a significant effect on the time to care, demography, injury causation, injury characteristics, volume and nature of admission, management and outcome of paediatric orthopaedic trauma patients. Objective: To document the effect of lockdown on the time to care at KGMU, use of ambulance, volume and type of admissions, demography, injury causation, injury characteristics, management and outcome of paediatric orthopaedic trauma patients. Methods:. This record review compared age, sex, type of admission, mechanism of injury, injury characteristics, type of treatment, vehicle used for transport, and outcome among patients admitted in pre-lockdown, lockdown and post lockdown. Results: Lockdown was associated with decrease in the number of cases (p<0.01), increase in the time since injury to reception (p<0.040), a rise in the share of referred admission (p<0.040), time since reception at KGMU, time to definitive care (p<0.001), high energy falls (p<0.001), injuries at home (p<0.001), higher ISS (p<0.001), non operative treatment (p=0.038) and greater use of ambulance (p=0.003). Conclusion: Lockdown resulted in a significant change in the causation and management of injury, significant delays in timeliness of care, reduction in the volume of admissions, an increase in injury severity and share of referral admissions. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

Impact of Air Quality Variations on COVID-19 Determinants: A Study of Four Selected Cities of India

Governments across the world are making considerable efforts in confronting COVID-19, from nationwide lockdowns to hygiene measures and maintaining social distancing. But at the same time, role of aerosols or/and the high concentrations of fine particulate matter or/and AQI levels in infection transmission and increasing the prevalence, morbidity and mortality of pandemic has been largely unexplored specifically in India where pollution attains peak in October and November every year. In the present study, we collected data regarding air quality index and COVID-19 determinants of four Indian cities : Bangalore, Delhi, Mumbai and Shillong from 1 October 2020 to 16 November 2020. We performed an analysis of variance on the regression model to estimate and quantify the strength of relationship between COVID-19 determinants and air pollution index (AQI). Results show that AQI has a significant impact on both response variables, that is COVID-19 cases as well as mortality (p < 0.05 at 95% confidence level) in Delhi, Mumbai and Bangalore (p < 0.05) but in Shillong no impact of AQI on COVID-19 cases and AQI (p = 0.343), as well as deaths (p = 0.664), was observed. We conclude that it is both conceivable and reasonable to suspect the role of increased AQI levels in aggravating COVID-19 morbidity and mortality. Thus, we recommend that critical meteorological conditions, like haze/smog caused by factors, like stubble burning or firing crackers should be predicted and monitored more systematically as they may lead to deterioration of respiratory problems. As the whole world is striving to fight against the deadly pandemic, it is extremely imperative to focus not only on human health as a part of response but also on global planetary health. Short term measures that can minimize supplementary risks, like adverse weather situations including pollution, poor air quality should be considered more meticulously and judiciously so that new flares of COVID-19 morbidity and mortality can be restricted. © 2022 - Kalpana Corporation.

A fractional-order delay differential SEIR model forCOVID-19: Stability analysis and Hopf bifurcation

In this manuscript, we study a fractional order time delay SEIR model of COVID-19 disease. Some conditions on stability and Hopf bifurcation have been derived for the model by using Laplace transformation. Further numerical simulation has been carried out for the purpose of better understanding of our results. © CSP - Cambridge, UK, I&S - Florida, USA, 2022

Coupling Wastewater-Based Epidemiological Surveillance and Modelling of SARS-COV-2/COVID-19: Practical Applications at the Public Health Agency of Canada (preprint)

Wastewater-based surveillance (WBS) of SARS-CoV-2 offers a complementary tool for clinical surveillance to detect and monitor Coronavirus Disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviors and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies. However, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outputs. The implementation of WBS therefore requires a comprehensive framework to outline an analysis pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities - Vancouver, Edmonton, Toronto, Montreal and Halifax - whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. This work provides insights on approaches to implement WBS at the national scale in an accurate and efficient manner. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.

Fijian Students' Reactions to Required Fully Online Courses during Covid-19

The recent outbreak of the global pandemic COVID-19 required Fiji National University offer fully online courses, which is a new form of pedagogy for many students. This new form of learning benefitted many students but created obstacles for others. The purpose of this study was to investigate student perceptions of the advantages and disadvantages of fully online courses due to the COVID-19 pandemic. An online survey in the form of a semi-structured questionnaire was used to gather data from 138 students. Data were analysed using thematic analysis. The study found that fully online learning suits students during pandemics, natural disasters (flooding, cyclones), and political upheavals. Fully online learning also helps students who have permanent employment and have difficulty getting time off to attend face-to-face classes. It also helps maritime or remote students who have limited access to the main centre or helps lecturer/facilitator deliver instructions when he/she is out of a country or is far away. The study also confirmed that fully online learning requires robust internet connectivity and a sustainable power supply allowing students to assess course materials from the comfort of their homes at their own pace. The student's safety in terms of travel and transmission of diseases are maintained. Fully online learning classes also assist students in saving fuel costs and rushing to the classes. © 2022 Eskisehir Osmangazi University. All rights reserved.

A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities.

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.

Multiplex RT-qPCR assay (N200) to detect and estimate prevalence of multiple SARS-CoV-2 Variants of Concern in wastewater (preprint)

Wastewater-based surveillance (WBS) has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Quantities of viral fragments of SARS-CoV-2 in wastewater are related to numbers of clinical cases of COVID-19 reported within the corresponding sewershed. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) or sequencing. A multiplex RT-qPCR assay to detect and estimate the prevalence of multiple VOCs, including Omicron/Alpha, Beta, Gamma, and Delta, in wastewater RNA extracts was developed and validated. The probe-based multiplex assay, named N200, focuses on amino acids 199-202, a region of the N gene that contains several mutations that are associated with variants of SARS-CoV-2 within a single amplicon. Each of the probes in the N200 assay are specific to the targeted mutations and worked equally well in single- and multi-plex modes. To estimate prevalence of each VOC, the abundance of the targeted mutation was compared with a non-mutated region within the same amplified region. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from six sewersheds in Ontario, Canada collected between December 1, 2021, and January 4, 2022. Using the N200 assay, the replacement of the Delta variant along with the introduction and rapid dominance of the Omicron variant were monitored in near real-time, as they occurred nearly simultaneously at all six locations. The N200 assay is robust and efficient for wastewater surveillance can be adopted into VOC monitoring programs or replace more laborious assays currently being used to monitor SARS-CoV-2 and its VOCs.

Coronavirus Infection Segmentation Detection Using UNET Deep Learning Architecture

The high potency and spread of the coronavirus pandemic has rapidly swept over a global scale, causing a large number of deaths and devastation. Its mutants have exaggerated the situation further, which has become a serious concern and a challenge for scientists, especially medical practitioners, to devise some advanced remedial actions. This paper intends to address this by developing a model based on deep learning for segmenting the affected regions in the lungs using CT-scan images. We propose a novel segmentation model based on UNet using Xception-Net in the encoder stage to detect covid-19 infection in CT-scans with two main aspects. It combines the local residual connections in the decoder unit of UNet with the typical global residual connections that lead to better performance. Also, the encoder component uses a pre-trained state-of-the-art feature extraction model that helps the system converge to the optimal value precisely due to the pre-trained weights. We apply a contrast-limited version of the adaptive histogram equalization in the data preparation stage to make the frequency of image pixels uniformly distributed. This decreases the biasedness in the model towards specific sections of CT-scans images. Our proposed model outperformed some existing counterparts, including TV-UNet, Inf-Net, ED-CNN. © 2021 IEEE.

A Sensitive and Rapid Wastewater Test for SARS-COV-2 and Its Use for the Early Detection of a Cluster of Cases in a Remote Community.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been used to monitor trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence in the community. A major challenge in establishing wastewater surveillance programs, especially in remote areas, is the need for a well-equipped laboratory for sample analysis. Currently, no options exist for rapid, sensitive, mobile, and easy-to-use wastewater tests for SARS-CoV-2. The performance of the GeneXpert system, which offers cartridge-based, rapid molecular clinical testing for SARS-CoV-2 in a portable platform, was evaluated using wastewater as the input. The GeneXpert demonstrated a SARS-CoV-2 limit of detection in wastewater below 32 copies/mL with a sample processing time of less than an hour. Using wastewater samples collected from multiple sites across Canada during February and March 2021, a high overall agreement (97.8%) was observed between the GeneXpert assay and laboratory-developed tests regarding the presence or absence of SARS-CoV-2. Additionally, with the use of centrifugal filters, the detection threshold of the GeneXpert system was improved to <10 copies/mL in wastewater. Finally, to support on-site wastewater surveillance, GeneXpert testing was implemented in Yellowknife, a remote community in Northern Canada, where its use successfully alerted public health authorities to undetected transmission of COVID-19. The identification of SARS-CoV-2 in wastewater triggered clinical testing of recent travelers and identification of new COVID-19 cases/clusters. Taken together, these results suggest that GeneXpert is a viable option for surveillance of SARS-CoV-2 in wastewater in locations that do not have access to established testing laboratories. IMPORTANCE Wastewater-based surveillance is a powerful tool that provides an unbiased measure of COVID-19 prevalence in a community. This work describes a sensitive wastewater rapid test for SARS-CoV-2 based on a widely distributed technology, the GeneXpert. The advantages of an easy-to-use wastewater test for SARS-CoV-2 are clear: it supports surveillance in remote communities, improves access to testing, and provides faster results allowing for an immediate public health response. The application of wastewater rapid testing in a remote community facilitated the detection of a COVID-19 cluster and triggered public health action, clearly demonstrating the utility of this technology. Wastewater surveillance will become increasingly important in the postvaccination pandemic landscape as individuals with asymptomatic/mild infections continue transmitting SARS-CoV-2 but are unlikely to be tested.

RT-qPCR detection of SARS-CoV-2 mutations S 69-70 del, S N501Y and N D3L associated with variants of concern in Canadian wastewater samples.

SARS-CoV-2 variants of concern (VoC) have been increasingly detected in clinical surveillance in Canada and internationally. These VoC are associated with higher transmissibility rates and in some cases, increased mortality. In this work we present a national wastewater survey of the distribution of three SARS-CoV-2 mutations found in the B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma) VoC, namely the S-gene 69-70 deletion, N501Y mutation, and N-gene D3L. RT-qPCR allelic discrimination assays were sufficiently sensitive and specific for detection and relative quantitation of SARS-CoV-2 variants in wastewater to allow for rapid population-level screening and surveillance. We tested 261 samples collected from 5 Canadian cities (Vancouver, Edmonton, Toronto, Montreal, and Halifax) and 6 communities in the Northwest Territories from February 16th to March 28th, 2021. VoC were not detected in the Territorial communities, suggesting the absence of VoC SARS-CoV-2 cases in those communities. Percentage of variant remained low throughout the study period in the majority of the sites tested, however the Toronto sites showed a marked increase from ~25% to ~75% over the study period. The results of this study highlight the utility of population level molecular surveillance of SARS-CoV-2 VoC using wastewater. Wastewater monitoring for VoC can be a powerful tool in informing public health responses, including monitoring trends independent of clinical surveillance and providing early warning to communities.

Impact of covid-19 pandemic and lockdown on the meat consumption pattern in india: A preliminary analysis

The people of India have a wide variety of eating habits that vary according to ethnicity, geography, and culture. The consumption of meat primarily covers the protein requirement of the Indians, and three out of four are non-vegetarians. There is a need to understand the impact of the COVID-19 pandemic and the associated countrywide lockdown on the meat consumption pattern of the Indian non-vegetarians. A countrywide survey was conducted among the consumers to study the impact of COVID-19 on the meat consumption pattern using a self-administered electronic questionnaire distributed through emails and online social networking platforms. A total of 416 responses were collected from the consumers belonging to different states and union territories. The data were analyzed as per the standard procedure. The meat consumption pattern of the non-vegetarians was found to be altered during the COVID-19 pandemic and the lockdown period. The majority of the consumers could not obtain a sufficient quantity of meat and meat products during the lockdown period due to various reasons such as the increased cost and decreased availability of livestock. The myths and rumors associated with meat consumption and the emergence of SARS-CoV-2 further weakened the meat trade in certain areas. Based on the findings of this survey, it is safe to confirm that the meat consumption pattern among Indian consumers was affected badly during the countrywide lockdown. © 2021, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.