Predicting COVID-19 Infected Individuals in a Defined Population from Wastewater RNA Data

Estimating the COVID-19 prevalence from wastewater

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

Biolayer-Interferometry-Guided Functionalization of Screen-Printed Graphene for Label-Free Electrochemical Virus Detection

Additive manufacturing holds promise for rapid prototyping and low-cost production of biosensors for diverse pathogens. Among additive manufacturing methods, screen printing is particularly desirable for high-throughput production of sensing platforms. However, this technique needs to be combined with carefully formulated inks, rapid postprocessing, and selective functionalization to meet all requirements for high-performance biosensing applications. Here, we present screen-printed graphene electrodes that are processed with thermal annealing to achieve high surface area and electrical conductivity for sensitive biodetection via electrochemical impedance spectroscopy. As a proof-of-concept, this biosensing platform is utilized for electrochemical detection of SARS-CoV-2. To ensure reliable specificity in the presence of multiple variants, biolayer interferometry (BLI) is used as a label-free and dynamic screening method to identify optimal antibodies for concurrent affinity to the Spike S1 proteins of Delta, Omicron, and Wild Type SARS-CoV-2 variants while maintaining low affinity to competing pathogens such as Influenza H1N1. The BLI-identified antibodies are robustly bound to the graphene electrode surface via oxygen moieties that are introduced during the thermal annealing process. The resulting electrochemical immunosensors achieve superior metrics including rapid detection (55 s readout following 15 min of incubation), low limits of detection (approaching 500 ag/mL for the Omicron variant), and high selectivity toward multiple variants. Importantly, the sensors perform well on clinical saliva samples detecting as few as 103 copies/mL of SARS-CoV-2 Omicron, following CDC protocols. The combination of the screen-printed graphene sensing platform and effective antibody selection using BLI can be generalized to a wide range of point-of-care immunosensors.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Quantification of SARS-CoV-2 in wastewater samples from hospitals treating COVID-19 patients during the first wave of the pandemic in Brazil

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

SARS-CoV-2 genetic material is removed during municipal wastewater treatment and is undetectable after advanced treatment

The aim of this study was to establish whether SARS-CoV-2 genetic material is detectable after municipal wastewater treatment and to verify its expected removal from purified water that is reclaimed for potable reuse. Viral loads of SARS-CoV-2 (N1 and N2 genes) were monitored in raw influent wastewater (sewage) entering a water reclamation facility and in subsequent advanced treatment. Despite the large viral RNA load in raw sewage during peak COVID-19 outbreaks, substantial amounts of SARS-CoV-2 genetic material were removed during the conventional wastewater treatment process. Further, SARS-CoV-2 genetic material was undetectable after advanced purification. This confirms that potable reuse is resilient against high viral loads which are expected results given the advanced degree of wastewater and water treatment. Findings from this study may enhance public perception of the safety of potable water reuse; however, it should also be noted that studies to date worldwide indicate no evidence of SARS-CoV-2 transmission via water, and the CDC does not consider fecal waste or wastewaters as a source of exposure.