Can a Paper-Based Device Trace COVID-19 Sources with Wastewater-Based Epidemiology?

Global impact of COVID-19 on food safety and environmental sustainability: Pathways to face the pandemic crisis

The COVID-19 pandemic poses ongoing challenges to the sustainability of various socioeconomic sectors, including agriculture, the food supply chain, the food business, and environmental sustainability. This study employs data obtained from the World Health Organization (WHO), and Food and Agriculture Organization (FAO), as well as scientific and technical research publications, to evaluate the impacts of COVID-19 on agriculture and food security. This article seeks to highlight the profound influence of the COVID-19 pandemic on agriculture, the supply and demand of food, and the overall safety of food. The article also explores the several pathways by which COVID-19 can be transmitted in these areas and the various technologies employed for its detection. The ongoing and post-pandemic ramifications are substantial since they could decrease agricultural output due to limitations on migration, a downturn in international trade, less buying capacity, and disturbances in food production and processing. Therefore, based on this thorough investigation, recommendations are issued for mitigating and controlling the pandemic's effects.

Systematic investigation and modeling prediction of virus inactivation by ozone in wastewater: Decoupling the matrix effects

Water disinfection is undoubtedly regarded as a critical step in ensuring the water safety for human consumption, and ozone is widely used as a highly effective disinfectant for the control of pathogenic microorganisms in water. Although the diminished ozone efficiencies in complex water matrices have been widely reported, the specific extent to which individual components of matrix act on the virus inactivation by ozone remains unclear, and effective methodologies to predict the comprehensive effects of various factors are needed. In this study, the decoupled impact of the intricate water matrix on the ozone inactivation of viruses was systematically investigated and assessed from a simulative perspective. The concept of "equivalent ozone depletion rate constant" (k') was introduced to quantify the influence of different species, and a kinetic model was developed based on the k' values for simulating the ozone inactivation processes in complex matrix. The mechanisms through which diverse species influenced the ozone inactivation effectiveness were identified: 1) competition effects (k' = 105∼107 M-1s-1), including organic matters and reductive ions (SO32-, NO2-, and I-), which were the most influential species inhibiting the virus inactivation; 2) shielding effects (k' = 103∼104 M-1s-1), including Ca2+, Mg2+, and kaolin; 3) insignificant effects (k' = 0∼1 M-1s-1), including Cl-, SO42-, NO3-, NH4+, and Br-; 4) promotion effects (k' = ∼-103 M-1s-1), including CO32- and HCO3-. Prediction of ozone disinfection efficiency and evaluation of species contribution under complex aquatic matrices were successfully realized utilizing the model. The systematic understanding and methodologies developed in this research provide a reliable framework for predicting ozone inactivation efficiency under complex matrix, and a potential tool for accurate disinfectant dosage determination and interfering factors control in actual wastewater treatment processes.

High-Throughput μPAD with Cascade Signal Amplification through Dual Enzymes for arsM in Paddy Soil

The arsM gene is a critical biomarker for the potential risk of arsenic exposure in paddy soil. However, on-site screening of arsM is limited by the lack of high-throughput point-of-use (POU) methods. Here, a multiplex CRISPR/Cas12a microfluidic paper-based analytical device (μPAD) was constructed for the high-throughput POU analysis of arsM, with cascade amplification driven by coupling crRNA-enhanced Cas12a and horseradish peroxidase (HRP)-modified probes. First, seven crRNAs were designed to recognize arsM, and their LODs and background signal intensities were evaluated. Next, a step-by-step iterative approach was utilized to develop and optimize coupling systems, which improved the sensitivity 32 times and eliminated background signal interference. Then, ssDNA reporters modified with HRP were introduced to further lower the LOD to 16 fM, and the assay results were visible to the naked eye. A multiplex channel microfluidic paper-based chip was developed for the reaction integration and simultaneous detection of 32 samples and generated a recovery rate between 87.70 and 114.05%, simplifying the pretreatment procedures and achieving high-throughput POU analysis. Finally, arsM in Wanshan paddy soil was screened on site, and the arsM abundance ranged from 1.05 × 106 to 6.49 × 107 copies/g; this result was not affected by the environmental indicators detected in the study. Thus, a coupling crRNA-based cascade amplification method for analyzing arsM was constructed, and a microfluidic device was developed that contains many more channels than previous paper chips, greatly improving the analytical performance in paddy soil samples and providing a promising tool for the on-site screening of arsM at large scales.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Rapid Environmental Monitoring, Capture, and Destruction Activities of SARS-CoV-2 and Bacterial Pathogens During the COVID-19 Health Emergency

BACKGROUND

The SARS-CoV-2 pandemic is a health emergency for occupational healthcare workers at COVID-19 hospital wards in Italy. The objective of the study was to investigate the bioreactor's effectivity in monitoring and improving air quality via detection, capture, and destruction of the SARS-CoV-2 virus and bacterial pathogens, reducing the risk of transmission among healthcare workers.

METHODS

Bioreactors are a demonstrated effective biomonitoring system. We implemented a methodological approach wherein they were placed at various hospitals treating COVID-19 patients in Italy. The detection of the SARS-CoV-2 virus was achieved through rapid biomonitoring testing of the solutes from the AIRcel bioreactors via SARS-CoV-2 rapid test antigen and consecutive reverse transcription-polymerase chain reaction (RT-PCR) analysis with the multiplex platform (XABT) and the real-time PCR rotor-gene.

RESULTS

The marked presence of the SARS-CoV-2 virus was found in multiple water samples via the detection of ORF1ab + N and/or E gene involved in gene expression and cellular signaling of the SARS-CoV-2 virus. The AIRcel bioreactors were able to neutralize the virus and bacterial pathogens effectively as traces of the viruses and bacteria were no longer found in multiple solute samples after an overnight period.

CONCLUSIONS

Transmission of COVID-19 via bioaerosols, transmitted by infected patients, remains a viable threat for health workers. AIRcel bioreactors allow for rapid biomonitoring testing for early virus detection within the environment, reducing the risk of exponential contagion exposure and maintaining good air quality without endangering health workers. This same protocol can also be extended to public spaces as a bio-monitoring hotspot tool for early detection.

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

SARS-CoV-2 surveillance in medical and industrial wastewater-a global perspective: a narrative review

The novel coronavirus SARS-CoV-2 has spread at an unprecedented rate since late 2019, leading to the global COVID-19 pandemic. During the pandemic, being able to detect SARS-CoV-2 in human populations with high coverage quickly is a huge challenge. As SARS-CoV-2 is excreted in human excreta and thus exposed to the aqueous environment through sewers, the goal is to develop an ideal, non-invasive, cost-effective epidemiological method for detecting SARS-CoV-2. Wastewater surveillance has gained widespread interest and is increasingly being investigated as an effective early warning tool for monitoring the spread and evolution of the virus. This review emphasizes important findings on SARS-CoV-2 wastewater-based epidemiology (WBE) in different continents and techniques used to detect SARS-CoV-2 in wastewater during the period 2020-2022. The results show that WBE is a valuable population-level method for monitoring SARS-CoV-2 and is a valuable early warning alert. It can assist policymakers in formulating relevant policies to avoid the negative impacts of early or delayed action. Such strategy can also help avoid unnecessary wastage of medical resources, rationalize vaccine distribution, assist early detection, and contain large-scale outbreaks.

The dynamics of early-stage transmission of COVID-19: A novel quantification of the role of global temperature

The global outbreak of COVID-19 has emerged as one of the most devastating and challenging threats to humanity. As many frontline workers are fighting against this disease, researchers are struggling to obtain a better understanding of the pathways and challenges of this pandemic. This paper evaluates the concept that the transmission of COVID-19 is intrinsically linked to temperature. Some complex nonlinear functional forms, such as the cubic function, are introduced to the empirical models to understand the interaction between temperature and the "growth" in the number of infected cases. An accurate quantitative interaction between temperature and the confirmed COVID-19 cases is obtained as log(Y) = -0.000146(temp_H)3 + 0.007410(temp_H)2 -0.063332 temp_H + 7.793842, where Y is the periodic growth in confirmed COVID-19 cases, and temp_H is the maximum daily temperature. This equation alone may be the first confirmed way to measure the quantitative interaction between temperature and human transmission of COVID-19. In addition, four important regions are identified in terms of maximum daily temperature (in Celsius) to understand the dynamics in the transmission of COVID-19 related to temperature. First, the transmission decreases within the range of -50 °C to 5.02 °C. Second, the transmission accelerates in the range of 5.02 °C to 16.92 °C. Essentially, this is the temperature range for an outbreak. Third, the transmission increases more slowly in the range of 16.92 °C to 28.82 °C. Within this range, the number of infections continues to grow, but at a slower pace. Finally, the transmission decreases in the range of 28.82 °C to 50 °C. Thus, according to this hypothesis, the threshold of 16.92 °C is the most critical, as the point at which the infection rate is the greatest. This result sheds light on the mechanism in the cyclicity of the ongoing COVID-19 pandemic worldwide. The implications of these results on policy issues are also discussed concerning a possible cyclical fluctuation pattern between the Northern and Southern Hemispheres.

Standardising policy and technology responses in the immediate aftermath of a pandemic: a comparative and conceptual framework

BACKGROUND

The initial policy response to the COVID-19 pandemic has differed widely across countries. Such variability in government interventions has made it difficult for policymakers and health research systems to compare what has happened and the effectiveness of interventions across nations. Timely information and analysis are crucial to addressing the lag between the pandemic and government responses to implement targeted interventions to alleviate the impact of the pandemic.

METHODS

To examine the effect government interventions and technological responses have on epidemiological and economic outcomes, this policy paper proposes a conceptual framework that provides a qualitative taxonomy of government policy directives implemented in the immediate aftermath of a pandemic announcement and before vaccines are implementable. This framework assigns a gradient indicating the intensity and extent of the policy measures and applies the gradient to four countries that share similar institutional features but different COVID-19 experiences: Italy, New Zealand, the United Kingdom and the United States of America.

RESULTS

Using the categorisation framework allows qualitative information to be presented, and more specifically the gradient can show the dynamic impact of policy interventions on specific outcomes. We have observed that the policy categorisation described here can be used by decision-makers to examine the impacts of major viral outbreaks such as SARS-CoV-2 on health and economic outcomes over time. The framework allows for a visualisation of the frequency and comparison of dominant policies and provides a conceptual tool to assess how dominant interventions (and innovations) affect different sets of health and non-health related outcomes during the response phase to the pandemic.

CONCLUSIONS

Policymakers and health researchers should converge toward an optimal set of policy interventions to minimize the costs of the pandemic (i.e., health and economic), and facilitate coordination across governance levels before effective vaccines are produced. The proposed framework provides a useful tool to direct health research system resources and build a policy benchmark for future viral outbreaks where vaccines are not readily available.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

Ultrasensitive Isothermal Detection of SARS-CoV-2 Based on Self-Priming Hairpin-Utilized Amplification of the G-Rich Sequence

The outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of fatalities all over the world. Unquestionably, the effective and timely testing for infected individuals is the most imperative for the prevention of the ongoing pandemic. Herein, a new method was established for detecting SARS-CoV-2 based on the self-priming hairpin-utilized isothermal amplification of the G-rich sequence (SHIAG). In this strategy, the target RNA binding to the hairpin probe (HP) was uniquely devised to lead to the self-priming-mediated extension followed by the continuously repeated nicking and extension reactions, consequently generating abundant G-rich sequences from the intended reaction capable of producing fluorescence signals upon specifically interacting with thioflavin T (ThT). Based on the unique isothermal design concept, we successfully identified SARS-CoV-2 genomic RNA (gRNA) as low as 0.19 fM with excellent selectivity by applying only a single HP and further verified its practical diagnostic capability by reliably testing a total of 100 clinical specimens for COVID-19 with 100% clinical sensitivity and specificity. This study would provide notable insights into the design and evolution of new isothermal strategies for the sensitive and facile detection of SARS-CoV-2 under resource constraints.

Wastewater-based epidemiology: a new frontier for tracking environmental persistence and community transmission of COVID-19

Recent research in many parts of the world has pointed towards evidence of SARS-CoV-2 RNA in both treated and raw municipal wastewater discharged by communities. Therefore, concerns regarding it being a possible enteric virus are abundant. Past history of SARS-CoV-1 outbreaks and viral survival information helps in establishing information regarding possible viral infectivity and survival of SARS-CoV-2. The paper examines the existing strategies and techniques including the efficacy of laboratory-based RT-qPCR technique for tracking environmental persistence and community transmission of COVID-19. Analysis of studies targeting untreated and treated wastewater as source of samples is carried out. The analysis shows that untreated samples were mostly positive for SARS-CoV-2 RNA in the target studies. Infectivity estimation from viral load data was found to be about two orders of magnitude higher than actual case data in one of the studies. Additionally, relevant research on environmental survivability of SARS-CoV-2 and possible gaps are examined. Biosensors and excretion metabolite tracking in viral detection are also examined, which hold tremendous importance for future research. Wastewater-based epidemiology (WBE) shows incredible promise in the near future for tracking environmental persistence and community transmission of highly infectious diseases such as SARS-CoV-2. With limited research available on SARS-CoV-2 with regard to WBE, it is imperative that focus be established on the evidence-based targeted studies.

Looking Forward: The Role of Academic Researchers in Building Sustainable Wastewater Surveillance Programs

BACKGROUND

In just over 2 years, tracking the COVID-19 pandemic through wastewater surveillance advanced from early reports of successful SARS-CoV-2 RNA detection in untreated wastewater to implementation of programs in at least 60 countries. Early wastewater monitoring efforts primarily originated in research laboratories and are now transitioning into more formal surveillance programs run in commercial and public health laboratories. A major challenge in this progression has been to simultaneously optimize methods and build scientific consensus while implementing surveillance programs, particularly during the rapidly changing landscape of the pandemic. Translating wastewater surveillance results for effective use by public health agencies also remains a key objective for the field.

OBJECTIVES

We examined the evolution of wastewater surveillance to identify model collaborations and effective partnerships that have created rapid and sustained success. We propose needed areas of research and key roles academic researchers can play in the framework of wastewater surveillance to aid in the transition from early monitoring efforts to more formalized programs within the public health system.

DISCUSSION

Although wastewater surveillance has rapidly developed as a useful public health tool for tracking COVID-19, there remain technical challenges and open scientific questions that academic researchers are equipped to address. This includes validating methodology and backfilling important knowledge gaps, such as fate and transport of surveillance targets and epidemiological links to wastewater concentrations. Our experience in initiating and implementing wastewater surveillance programs in the United States has allowed us to reflect on key barriers and draw useful lessons on how to promote synergy between different areas of expertise. As wastewater surveillance programs are formalized, the working relationships developed between academic researchers, commercial and public health laboratories, and data users should promote knowledge co-development. We believe active involvement of academic researchers will contribute to building robust surveillance programs that will ultimately provide new insights into population health. https://doi.org/10.1289/EHP11519.

Integration of RT-LAMP and Microfluidic Technology for Detection of SARS-CoV-2 in Wastewater as an Advanced Point-of-Care Platform

Development of lab-on-a-chip (LOC) system based on integration of reverse transcription loop-mediated isothermal amplification (RT-LAMP) and microfluidic technology is expected to speed up SARS-CoV-2 diagnostics allowing early intervention. In the current work, reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) and RT-LAMP assays were performed on extracted RNA of seven wastewater samples from COVID-19 hotspots. RT‑LAMP assay was also performed on wastewater samples without RNA extraction. Current detection of SARS-CoV-2 is mainly by RT-qPCR of ORF (ORF1ab) and N genes so we targeted both to find the best target gene for SARS-CoV-2 detection. We also performed RT-LAMP with/without RNA extraction inside microfluidic device to target both genes. Positivity rates of RT-qPCR and RT-LAMP performed on extracted RNA were 100.0% (7/7) and 85.7% (6/7), respectively. RT-qPCR results revealed that all 7 wastewater samples were positive for N gene (Ct range 37-39), and negative for ORF1ab, suggesting that N gene could be the best target gene for SARS-CoV-2 detection. RT-LAMP of N and ORF (ORF1a) genes performed on wastewater samples without RNA extraction indicated that all 7 samples remains pink (negative). The color remains pink in all microchannels except microchannels which subjected to RT-LAMP for targeting N region after RNA extraction (yellow color) in 6 out of 7 samples. This study shows that SARS-CoV-2 was successfully detected from wastewater samples using RT-LAMP in microfluidic chips. This study brings the novelty involving the use of wastewater samples for detection of SARS-CoV-2 without previous virus concentration and with/without RNA extraction.

Wastewater research and surveillance: an ethical exploration

The current COVID-19 pandemic has given wastewater research a huge impetus. While wastewater research has some promising applications, there are as yet no well-developed ethical guidelines on how and under what conditions to use wastewater research. The current perspective paper aims to explore the different ethical questions pertaining to wastewater research and surveillance and to provide some tentative guidelines on the desirability of different types of applications. This paper shows that wastewater research offers interesting possibilities, but that legal regulation and ethical guidelines are still lacking, while there are ethical risks involved. The perspective indicates that it is important to look beyond the regulation of data collection and to shift the focus to the question how the analysis and use of wastewater data can be supervised.

Biosensors for the detection of disease outbreaks through wastewater-based epidemiology

Wastewater-Based Epidemiology (WBE) is a novel community-wide monitoring tool that provides comprehensive real-time data of the public and environmental health status and can contribute to public health interventions, including those related to infectious disease outbreaks (e.g., the ongoing COVID-19 pandemic). Nonetheless, municipalities without centralized laboratories are likely still not able to process WBE samples. Biosensors are a potentially cost-effective solution to monitor the development of diseases through WBE to prevent local outbreaks. This review discusses the economic and technical feasibility of eighteen recently developed biosensors for the detection and monitoring of infectious disease agents in wastewater, prospecting the prevention of future pandemics.

Paper Device Combining CRISPR/Cas12a and Reverse-Transcription Loop-Mediated Isothermal Amplification for SARS-CoV-2 Detection in Wastewater

Wastewater-based surveillance of the COVID-19 pandemic holds great promise; however, a point-of-use detection method for SARS-CoV-2 in wastewater is lacking. Here, a portable paper device based on CRISPR/Cas12a and reverse-transcription loop-mediated isothermal amplification (RT-LAMP) with excellent sensitivity and specificity was developed for SARS-CoV-2 detection in wastewater. Three primer sets of RT-LAMP and guide RNAs (gRNAs) that could lead Cas12a to recognize target genes via base pairing were used to perform the high-fidelity RT-LAMP to detect the N, E, and S genes of SARS-CoV-2. Due to the trans-cleavage activity of CRISPR/Cas12a after high-fidelity amplicon recognition, carboxyfluorescein-ssDNA-Black Hole Quencher-1 and carboxyfluorescein-ssDNA-biotin probes were adopted to realize different visualization pathways via a fluorescence or lateral flow analysis, respectively. The reactions were integrated into a paper device for simultaneously detecting the N, E, and S genes with limits of detection (LODs) of 25, 310, and 10 copies/mL, respectively. The device achieved a semiquantitative analysis from 0 to 310 copies/mL due to the different LODs of the three genes. Blind experiments demonstrated that the device was suitable for wastewater analysis with 97.7% sensitivity and 82% semiquantitative accuracy. This is the first semiquantitative endpoint detection of SARS-CoV-2 in wastewater via different LODs, demonstrating a promising point-of-use method for wastewater-based surveillance.

Occurrence of SARS-CoV-2 in excreta, sewage, and environment: epidemiological significance and potential risks

The detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients' excreta raises the issue of its occurrence and fate in sewage. This review has focused on the presence of the SARS-CoV-2 in human excreta, wastewater, sewage sludge, and river waters. It explored the potential use of the wastewater-based epidemiology approach to report on the situation of current and eventual future SARS-CoV-2 outbreaks. The main concern of the occurrence of SARS-CoV-2 in the environment is the public health risks at sites of sewage products disposal and reuse, especially in low-income countries with inadequate sanitation, where direct discharge and reuse of raw sewage are common practices. The review also addressed the role sewage-irrigated agriculture can have in SARS-CoV-2 spread in the environmental compartments reached through sewage products application. An overview was made on the interest of sewage management, water safety, and hygienic practices for controlling the environmental dissemination of SARS-CoV-2.

Can wastewater surveillance assist China to cost-effectively prevent the nationwide outbreak of COVID-19?

China has controlled the nationwide spread of COVID-19 since April 2020, but it is still facing an enormous threat of disease resurgence originating from infected international travelers. Taking the rapid transmission and the mutation of SARS-CoV-2 into consideration, the current status would be easily jeopardized if sporadic locally-transmitted individuals are not identified at an early stage. Clinical diagnosis is the gold standard for COVID-19 surveillance, but it is hard to screen presymptomatic or asymptomatic cases in those who have not exhibited symptoms. Since presymptomatic or asymptomatic individuals are infectious, it is urgent to establish a surveillance system based on other tools that can profile the entire population. Infected people including those who are symptomatic, presymptomatic, and asymptomatic shed SARS-CoV-2 RNA in feces and thereby endow wastewater-based epidemiology (WBE) with an early-warning ability for mass COVID-19 surveillance. In the context of China's "COVID-zero" strategy, this work intends to discuss the practical feasibility of WBE applications as an early warning and disease surveillance system in hopes that WBE together with clinical testing would cost-effectively restrain sporadic COVID-19 outbreaks in China.

Sampling strategies for wastewater surveillance: Evaluating the variability of SARS-COV-2 RNA concentration in composite and grab samples

The shedding of SARS-CoV-2 RNA titers by infected individuals, even asymptomatic and oligosymptomatic ones, allows the use of wastewater monitoring to track the COVID-19 spread in a community. This approach is interesting especially for emerging countries with limited clinical testing capabilities. However, there are still important methodological aspects that need validation so that wastewater monitoring data become more representative and useful for public health. This study evaluated the between-day and within-day variability of SARS-CoV-2 RNA concentrations in 24-hour composite and grab samples from three different sampling points, including two wastewater treatment plants (WTTP) and a sewer manhole. In the between-day evaluation (17 weeks of monitoring), a good agreement between the SARS-CoV-2 RNA concentration of each sampling method was observed. There were no significant differences between the mean concentrations of the grab and composite samples (p-value > 0.05), considering N1 and N2 gene assays. The strong relationship between composite and grab samples was proven by correlation coefficients: Pearson's r of 0.83 and Spearman's rho of 0.78 (p-value < 0.05). In within-day evaluation, 24-hour cycles were analyzed and low variability in hourly viral concentrations was observed for three sampling points. The coefficient of variation (CV) values ranged from 3.0% to 11.5%. Overall, 24-hour profiles showed that viral RNA concentrations had less variability and greater agreement with the mean values between 8 a.m. and 10 a.m, the recommended time for grab sampling. Therefore, this study provides important information on wastewater sampling techniques for COVID-19 surveillance. Wastewater monitoring information will only be useful to public health and decision-makers if we ensure data quality through best practices.

A review on the contamination of SARS-CoV-2 in water bodies: Transmission route, virus recovery and recent biosensor detection techniques

The discovery of SARS-CoV-2 virus in the water bodies has been reported, and the risk of virus transmission to human via the water route due to poor wastewater management cannot be disregarded. The main source of the virus in water bodies is the sewage network systems which connects to the surface water. Wastewater-based epidemiology has been applied as an early surveillance tool to sense SARS-CoV-2 virus in the sewage network. This review discussed possible transmission routes of the SARS-CoV-2 virus and the challenges of the existing method in detecting the virus in wastewater. One significant challenge for the detection of the virus is that the high virus loading is diluted by the sheer volume of the wastewater. Hence, virus preconcentration from water samples prior to the application of virus assay is essential to accurately detect traceable virus loading. The preparation time, materials and conditions, virus type, recovery percentage, and various virus recovery techniques are comprehensively discussed in this review. The practicability of molecular methods such as Polymer-Chain-Reaction (PCR) for the detection of SARS-CoV-2 in wastewater will be revealed. The conventional virus detection techniques have several shortcomings and the potential of biosensors as an alternative is also considered. Biosensing techniques have also been proposed as an alternative to PCR and have reported detection limits of 10 pg/μl. This review serves to guide the reader on the future designs and development of highly sensitive, robust and, cost effective SARS-CoV-2 lab-on-a-chip biosensors for use in complex wastewater.

Smart nanofibres for specific and ultrasensitive nanobiosensors and drug delivery systems

Biosensors are dynamically developing analytical devices for the detection of substrates or other bioactive substances. They can be used for quick gas or liquid analyses and the construction of sensitive detection systems. This review highlights the advances and development of biosensors suitable for human and veterinary medicine and, namely, a novel contribution of nanotechnology for ultrasensitive diagnosis and personalized medicine. The synergic effect of nanotechnology and biosensors opens a new dimension for effective treatment and disease detection at their early stages.

A Mobile Laboratory Enables Fecal Pollution Source Tracking in Catchments Using Onsite qPCR Assays

Onsite molecular diagnostics can revolutionize fecal pollution source tracking. We aimed to validate a method for onsite qPCR assays with a miniature speaker-sized Q qPCR instrument and other portable equipment items. We showed that marker genes for total bacteria (16S) and E. coli (rodA) in 100 mL of river water measured with this method agreed within ±0.3 log10 units with results obtained when using conventional laboratory equipment items. We then deployed the portable method in a mobile laboratory (‘lab in a van’) and quantified HF183 marker genes for human host associated Bacteroides in river water within 3 h of sampling. We also used the mobile laboratory to investigate urban river water and effluents from two storm drains and a retention pond and collected comprehensive microbial and physicochemical water quality data. We found significantly higher HF183 gene levels in the older storm drain compared to the river water (6.03 ± 0.04 vs. 4.23 ± 0.03 log10 gene copies per 100 mL), and a principal component analysis revealed that storm drain effluent retention in a pond beneficially altered water characteristics, making them more like those of the receiving river. In conclusion, onsite qPCR assays can be performed with portable equipment items to quickly test water.

Remediation of surface water contaminated by pathogenic microorganisms using calcium peroxide: Matrix effect, micro-mechanisms and morphological-physiological changes

Calcium peroxide (CaO₂), a common solid peroxide, has been increasingly used in contaminated site remediation due to its ability to release oxygen (O₂) and hydrogen peroxide (H₂O₂) and its environmental friendliness. Our present study is first to explore micromechnisms of CaO₂ to efficaciously inactivate pathogen indicators including gram-negative bacterium of Escherichia coli (E. coli), gram-positive bacterium of Staphylococcus aureus (S. aureus), and virus of Escherichia coli-specific M13 bacteriophage (VCSM13) under low concentration (≤ 4 mmol L^-1 (mM)). The inactivation mechanisms of E. coli, S. aureus (1 mmol L^-1 CaO₂) and VCSM13 (4 mmol L^-1) were mainly attributed to OH^- (32∼58%) and •OH (34∼42%), followed by H₂O₂ (13∼20%) and O₂^•- (10∼12%) generated from CaO₂, with the observed morphological and physiological-associated damages. Also, average steady-state concentrations of (OH^-, •OH, H₂O₂, and O₂^•-) and their reaction rate constants with E. coli and VCSM13 were determined. Accordingly, the micro-mechanism model of inactivation was established and validated, and the inactivation efficiency of the same order of magnitude of pathogen was predicted. Furthermore, during the common environmental factors, the copper ions was found to be promote CaO₂ inactivation of pathogens, and dissolved organic matter (DOM) fractions had a negative effect on CaO₂ inactivation. The present study explored the mechanisms of CaO₂ inactivation of pathogens in real surface water, laying the foundation for its potential use in the inactivation of water-borne microbial pathogens.

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

[Innovative SARS-CoV-2 crisis management in the public health sector: Corona dashboard and wastewater surveillance using the example of Berchtesgadener Land, Germany]

BACKGROUND

The rise of an infectious disease crisis such as the SARS-CoV‑2 pandemic posed significant challenges for the administrative structures of the public health service, which resulted in varying levels of efficiency in outbreak management as a function of staffing and digital resources. This substantially impeded the integration of innovative pandemic outbreak management tools. Innovative crisis management, such as cluster tracking, risk group testing, georeferencing, or the integration of wastewater surveillance recommended by the EU Commission, was made significantly more difficult.

AIM

In this case study in Berchtesgadener Land, we present the integration of an area-wide georeferenced wastewater surveillance system that captured 95% of the entire population since November 2020.

METHODOLOGY

Sampling occurred twice a week at nine municipal wastewater treatment plants and directly from the main sewer at three locations. Samples were pre-treated by centrifugation and subsequently analyzed by digital droplet polymerase chain reaction (PCR) targeting four specific genes of SARS-CoV‑2.

RESULTS

The integration of an area-wide georeferenced wastewater surveillance system was successful. Wastewater occurrences are plotted for each municipality against cumulative infections over seven days per 100,000 inhabitants. Changes in the infection pattern in individual communities are noticeable ten days ahead of the official case numbers with a sensitivity of approximately 20 in 100,000 inhabitants.

DISCUSSION

The integration of this innovative approach to provide a comprehensive overview of the situation by employing a digital dashboard and the use of an early warning system via quantitative wastewater surveillance resulted in very efficient, proactive management, which might serve as a blueprint for other municipalities in Germany.

G. G, Moharir S, Mishra RK, Mohan SV. Longitudinal and Long-Term Wastewater Surveillance for COVID-19: Infection Dynamics and Zoning of Urban Community

Wastewater-based epidemiology (WBE) is emerging as a potential approach to study the infection dynamics of SARS-CoV-2 at a community level. Periodic sewage surveillance can act as an indicative tool to predict the early surge of pandemic within the community and understand the dynamics of infection and, thereby, facilitates for proper healthcare management. In this study, we performed a long-term epidemiological surveillance to assess the SARS-CoV-2 spread in domestic sewage over one year (July 2020 to August 2021) by adopting longitudinal sampling to represent a selected community (~2.5 lakhs population). Results indicated temporal dynamics in the viral load. A consistent amount of viral load was observed during the months from July 2020 to November 2020, suggesting a higher spread of the viral infection among the community, followed by a decrease in the subsequent two months (December 2020 and January 2021). A marginal increase was observed during February 2021, hinting at the onset of the second wave (from March 2021) that reached it speak in April 2021. Dynamics of the community infection rates were calculated based on the viral gene copies to assess the severity of COVID-19 spread. With the ability to predict the infection spread, longitudinal WBE studies also offer the prospect of zoning specific areas based on the infection rates. Zoning of the selected community based on the infection rates assists health management to plan and manage the infection in an effective way. WBE promotes clinical inspection with simultaneous disease detection and management, in addition to an advance warning signal to anticipate outbreaks, with respect to the slated community/zones, to tackle, prepare for and manage the pandemic.

Risks and new challenges in the food chain: Viral contamination and decontamination from a global perspective, guidelines, and cleaning

Even during the continuing world pandemic of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), consumers remain exposed to the risk of getting infected by existing, emerging, or re-emerging foodborne and waterborne viruses. SARS-CoV-2 is different in that it is transmitted directly via the airborne route (droplets and aerosols) or indirect contact (surfaces contaminated with SARS-CoV-2). International food and health organizations and national regulatory bodies have provided guidance to protect individuals active in food premises from potential occupational exposure to SARS-CoV-2, and have recommended chemicals effective in controlling the virus. Additionally, to exclude transmission of foodborne and waterborne viruses, hygiene practices to remove viral contaminants from surfaces are applied in different stages of the food chain (e.g., food plants, food distribution, storage, retail sector, etc.), while new and enhanced measures effective in the control of all types of viruses are under development. This comprehensive review aims to analyze and compare efficacies of existing cleaning practices currently used in the food industry to remove pathogenic viruses from air, nonfood, and food contact surfaces, as well as from food surfaces. In addition, the classification, modes of transmission, and survival of food and waterborne viruses, as well as SARS-CoV-2 will be presented. The international guidelines and national regulations are summarized in terms of virucidal chemical agents and their applications.

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems, and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as potential reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.

COVID-19 and the UK water sector: Exploring organizational responses through a resilience framework

The unprecedented scale and impact of the COVID-19 pandemic have required organizations to adapt all facets of their operations. The impact on the UK water sector extends beyond engineering and treatment processes, with social, economic and environmental consequences. Semi-structured interviews were conducted with executives from 10 UK water companies to investigate the organizational response to the pandemic, and how their response impacted operational delivery. The Safe and SuRe framework was used to structure interview questions and analysis. Emergent themes of changes to customer behaviour, changes to operational practices and industry collaboration were mapped onto the framework and a ripple effect map developed. Lessons learnt highlight a failure to adequately prepare for the scale of the threat, the success of sector-level collaboration and a need to embrace new ways of working.

Towards detection of SARS-CoV-2 RNA in human saliva: A paper-based cell-free toehold switch biosensor with a visual bioluminescent output

The COVID-19 pandemic has illustrated the global demand for rapid, low-cost, widely distributable and point-of-care nucleic acid diagnostic technologies. Such technologies could help disrupt transmission, sustain economies and preserve health and lives during widespread infection. In contrast, conventional nucleic acid diagnostic procedures require trained personnel, complex laboratories, expensive equipment, and protracted processing times. In this work, lyophilized cell-free protein synthesis (CFPS) and toehold switch riboregulators are employed to develop a promising paper-based nucleic acid diagnostic platform activated simply by the addition of saliva. First, to facilitate distribution and deployment, an economical paper support matrix is identified and a mass-producible test cassette designed with integral saliva sample receptacles. Next, CFPS is optimized in the presence of saliva using murine RNase inhibitor. Finally, original toehold switch riboregulators are engineered to express the bioluminescent reporter NanoLuc in response to SARS-CoV-2 RNA sequences present in saliva samples. The biosensor generates a visible signal in as few as seven minutes following administration of 15 μL saliva enriched with high concentrations of SARS-CoV-2 RNA sequences. The estimated cost of this test is less than 0.50 USD, which could make this platform readily accessible to both the developed and developing world. While additional research is needed to decrease the limit of detection, this work represents important progress toward developing a diagnostic technology that is rapid, low-cost, distributable and deployable at the point-of-care by a layperson.

SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.

Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.

Monitoring the Viral Transmission of SARS-CoV-2 in Still Waterbodies Using a Lanthanide-Doped Carbon Nanoparticle-Based Sensor Array

The latest epidemic of extremely infectious coronavirus disease 2019 (COVID-19) has created a significant public health concern. Despite substantial efforts to contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within a specific location, shortcomings in the surveillance of predominantly asymptomatic infections constrain attempts to identify the epidemiological spread of the virus. Continuous surveillance of wastewater streams, including sewage, offers opportunities to track the spread of SARS-CoV-2, which is believed to be found in fecal waste. To demonstrate the feasibility of SARS-CoV-2 detection in wastewater systems, we herein present a novel facilely constructed fluorescence sensing array based on a panel of three different lanthanide-doped carbon nanoparticles (LnCNPs). The differential fluorescence response pattern due to the counterion-ligand interactions allowed us to employ powerful pattern recognition to effectively detect SARS-CoV-2 and differentiate it from other viruses or bacteria. The sensor results were benchmarked to the gold standard RT-qPCR, and the sensor showed excellent sensitivity (1.5 copies/μL) and a short sample-to-results time of 15 min. This differential response of the sensor array was also explained from the differential mode of binding of the LnCNPs with the surface proteins of the studied bacteria and viruses. Therefore, the developed sensor array provides a cost-effective, community diagnostic tool that could be potentially used as a novel epidemiologic surveillance approach to mitigate the spread of COVID-19.

Sorption of SARS-CoV-2 Virus Particles to the Surface of Microplastics Released during Washing Processes

The research aims at washing processes as possible sources of microplastics, specifical microfibers in wastewater, and the behavior of the virus particles SARS-CoV-2 in wastewater after the washing process as well as their ability to sorb to the surface of microfibers, released from washing processes. The conclusions of the research point to the ability of the virus to attach to possible solid impurities such as textile fibers (microfibers) occurring in the sewer and to the ability of wash water to influence their possible occurrence in the sewer. The highest efficiency (more than 99%) of removal virus particles was after washing process, using liquid washing powder, and washing soda. These findings may gradually contribute to a better understanding of the behavior of the virus particles in the sewer.

Molecular Methods for Pathogenic Bacteria Detection and Recent Advances in Wastewater Analysis

With increasing concerns about public health and the development of molecular techniques, new detection tools and the combination of existing approaches have increased the abilities of pathogenic bacteria monitoring by exploring new biomarkers, increasing the sensitivity and accuracy of detection, quantification, and analyzing various genes such as functional genes and antimicrobial resistance genes (ARG). Molecular methods are gradually emerging as the most popular detection approach for pathogens, in addition to the conventional culture-based plate enumeration methods. The analysis of pathogens in wastewater and the back-estimation of infections in the community, also known as wastewater-based epidemiology (WBE), is an emerging methodology and has a great potential to supplement current surveillance systems for the monitoring of infectious diseases and the early warning of outbreaks. However, as a complex matrix, wastewater largely challenges the analytical performance of molecular methods. This review synthesized the literature of typical pathogenic bacteria in wastewater, types of biomarkers, molecular methods for bacterial analysis, and their recent advances in wastewater analysis. The advantages and limitation of these molecular methods were evaluated, and their prospects in WBE were discussed to provide insight for future development.

Challenges and emerging perspectives of an international SARS-CoV-2 epidemiological surveillance in wastewater

SARS-CoV-2 is a new type of coronavirus capable to infect humans and cause the severe acute respiratory syndrome COVID-19, a disease that has been causing huge impacts across the Earth. COVID-19 patients, including mild, pre-symptomatic and asymptomatic cases, were often seen to contain infectious fragments of SARS-CoV-2 in feces and urine samples. Therefore, studies to detect the new coronavirus in wastewater, which collect and concentrate human excreta, have been extremely useful as a viral tracking tool in communities. This type of monitoring, in addition to serve as a non-invasive early warning of COVID-19 outbreaks, would provide better predictions about the SARS-CoV-2 spread and strongly contribute to maintenance the global health. Although current methods to detect viruses in wastewater, based on molecular RT-PCR and RT-qPCR techniques, were considered as reliable and provided accurate qualitative and quantitative results, they have been facing considerable challenges concerning the SARS-CoV-2 surveillance. In this review, the methods used to detect the SARS-CoV-2 in wastewater and the challenges to implement an international viral monitoring network were described. The article also addressed the emerging perspectives associated with the SARS-CoV-2 epidemiological surveillance in this environment and the importance of a worldwide collaboration to generate and disseminate the detection results.

Wastewater-based epidemiology-surveillance and early detection of waterborne pathogens with a focus on SARS-CoV-2, Cryptosporidium and Giardia

Waterborne diseases are a major global problem, resulting in high morbidity and mortality, and massive economic costs. The ability to rapidly and reliably detect and monitor the spread of waterborne diseases is vital for early intervention and preventing more widespread disease outbreaks. Pathogens are, however, difficult to detect in water and are not practicably detectable at acceptable concentrations that need to be achieved in treated drinking water (which are of the order one per million litre). Furthermore, current clinical-based surveillance methods have many limitations such as the invasive nature of the testing and the challenges in testing large numbers of people. Wastewater-based epidemiology (WBE), which is based on the analysis of wastewater to monitor the emergence and spread of infectious disease at a population level, has received renewed attention in light of the current coronavirus disease 2019 (COVID-19) pandemic. The present review will focus on the application of WBE for the detection and surveillance of pathogens with a focus on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the waterborne protozoan parasites Cryptosporidium and Giardia. The review highlights the benefits and challenges of WBE and the future of this tool for community-wide infectious disease surveillance.

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (µPADs) to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene. The µPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (∼40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

On the Critical Role of Human Feces and Public Toilets in the Transmission of COVID-19: Evidence from China

The surprising spread speed of the COVID-19 pandemic creates an urgent need for investigating the transmission chain or transmission pattern of COVID-19 beyond the traditional respiratory channels. This study therefore examines whether human feces and public toilets play a critical role in the transmission of COVID-19. First, it develops a theoretical model that simulates the transmission chain of COVID-19 through public restrooms. Second, it uses stabilized epidemic data from China to empirically examine this theory, conducting an empirical estimation using a two-stage least squares (2SLS) model with appropriate instrumental variables (IVs). This study confirms that the wastewater directly promotes the transmission of COVID-19 within a city. However, the role of garbage in this transmission chain is more indirect in the sense that garbage has a complex relationship with public toilets, and it promotes the transmission of COVID-19 within a city through interaction with public toilets and, hence, human feces. These findings have very strong policy implications in the sense that if we can somehow use the ratio of public toilets as a policy instrument, then we can find a way to minimize the total number of infections in a region. As shown in this study, pushing the ratio of public toilets (against open defecation) to the local population in a city to its optimal level would help to reduce the total infection in a region.

Prevalence of human pathogenic viruses in wastewater: A potential transmission risk as well as an effective tool for early outbreak detection for COVID-19

Millions of human pathogenic viral particles are shed from infected individuals and introduce into wastewater, subsequently causing waterborne diseases worldwide. These viruses can be transmitted from wastewater to human beings via direct contact and/or ingestion/inhalation of aerosols. Even the advanced wastewater treatment technologies are unable to remove pathogenic viruses from wastewater completely, posing a serious health risk. Recently, coronavirus disease 2019 (COVID-19) has been urged globally due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has resulted in >4.1 million deaths until July 2021. A rapid human-to-human transmission, uncertainties in effective vaccines, non-specific medical treatments, and unclear symptoms compelled the world into complete lockdown, social distancing, air-travel suspension, and closure of educational institutions, subsequently damaging the global economy and trade. Although, few medical treatments, rapid detection tools, and vaccines have been developed so far to curb the spread of COVID-19; however, several uncertainties exist in their applicability. Further, the acceptance of vaccines among communities is lower owing to the fear of side effects such as blood-clotting and heart inflammation. SARS-CoV-2, an etiologic agent of COVID-19, has frequently been detected in wastewater, depicting a potential transmission risk to healthy individuals. Contrarily, the occurrence of SARS-CoV-2 in wastewater can be used as an early outbreak detection tool via water-based epidemiology. Therefore, the spread of SARS-CoV-2 through fecal-oral pathway can be reduced and any possible outbreak can be evaded by proper wastewater surveillance. In this review, wastewater recycling complications, potential health risks of COVID-19 emergence, and current epidemiological measures to control COVID-19 spread have been discussed. Moreover, the viability of SARS-CoV-2 in various environments and survival in wastewater has been reviewed. Additionally, the necessary actions (vaccination, face mask, social distancing, and hand sanitization) to limit the transmission of SARS-CoV-2 have been recommended. Therefore, wastewater surveillance can serve as a feasible, efficient, and reliable epidemiological measure to lessen the spread of COVID-19.

Diagnosis of COVID-19, vitality of emerging technologies and preventive measures

Coronavirus diseases-2019 (COVID-19) is becoming increasing serious and major threat to public health concerns. As a matter of fact, timely testing enhances the life-saving judgments on treatment and isolation of COVID-19 infected individuals at possible earliest stage which ultimately suppresses spread of infectious diseases. Many government and private research institutes and manufacturing companies are striving to develop reliable tests for prompt quantification of SARS-CoV-2. In this review, we summarize existing diagnostic methods as manual laboratory-based nucleic acid assays for COVID-19 and their limitations. Moreover, vitality of rapid and point of care serological tests together with emerging biosensing technologies has been discussed in details. Point of care tests with characteristics of rapidity, accurateness, portability, low cost and requiring non-specific devices possess great suitability in COVID-19 diagnosis and detection. Besides, this review also sheds light on several preventive measures to track and manage disease spread in current and future outbreaks of diseases.

Challenges to detect SARS-CoV-2 on environmental media, the need and strategies to implement the detection methodologies in wastewaters

Understanding risks, putting in place preventative methods to seamlessly continue daily activities are essential tools to fight a pandemic. All social, commercial and leisure activities have an impact on the environmental media. Therefore, to accurately predict the fate and behavior of viruses in the environment, it is necessary to understand and analyze available detection methods, possible transmission pathways and preventative techniques. The aim of this review is to critically analyze and summarize the research done regarding SARS-COV-2 virus detection, focusing on sampling and laboratory detection methods in environmental media. Special attention will be given to wastewater and sewage sludge. This review has summarized the survival of the virus on surfaces to estimate the risk carried by different environmental media (water, wastewater, air and soil) in order to explain which communities are under higher risk. The critical analysis concludes that the detection of SARS-CoV-2 with current technologies and sampling strategies would reveal the presence of the virus. This information could be used to design systematic sampling points throughout the sewage systems when available, taking into account peak flows and more importantly economic factors on when to sample. Such approaches will provide clues for potential future viral outbreak, saving financial resources by reducing testing necessities for viral detection, hence contributing for more appropriate confinement policies by governments and could be further used to define more precisely post-pandemic or additional waves measures if/ when needed.

Rolling Circle Amplification as an Efficient Analytical Tool for Rapid Detection of Contaminants in Aqueous Environments

Environmental contaminants are a global concern, and an effective strategy for remediation is to develop a rapid, on-site, and affordable monitoring method. However, this remains challenging, especially with regard to the detection of various contaminants in complex water environments. The application of molecular methods has recently attracted increasing attention; for example, rolling circle amplification (RCA) is an isothermal enzymatic process in which a short nucleic acid primer is amplified to form a long single-stranded nucleic acid using a circular template and special nucleic acid polymerases. Furthermore, this approach can be further engineered into a device for point-of-need monitoring of environmental pollutants. In this paper, we describe the fundamental principles of RCA and the advantages and disadvantages of RCA assays. Then, we discuss the recently developed RCA-based tools for environmental analysis to determine various targets, including heavy metals, organic small molecules, nucleic acids, peptides, proteins, and even microorganisms in aqueous environments. Finally, we summarize the challenges and outline strategies for the advancement of this technique for application in contaminant monitoring.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

Wastewater Based Epidemiology Perspective as a Faster Protocol for Detecting Coronavirus RNA in Human Populations: A Review with Specific Reference to SARS-CoV-2 Virus

Wastewater-based epidemiology (WBE) has a long history of identifying a variety of viruses from poliovirus to coronaviruses, including novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The presence and detection of SARS-CoV-2 in human feces and its passage into the water bodies are significant public health challenges. Hence, the hot issue of WBE of SARS-CoV-2 in the coronavirus respiratory disease (COVID-19) pandemic is a matter of utmost importance (e.g., SARS-CoV-1). The present review discusses the background, state of the art, actual status, and prospects of WBE, as well as the detection and quantification protocols of SARS-CoV-2 in wastewater. The SARS-CoV-2 detection studies have been performed in different water matrixes such as influent and effluent of wastewater treatment plants, suburban pumping stations, hospital wastewater, and sewer networks around the globe except for Antarctica. The findings revealed that all WBE studies were in accordance with clinical and epidemiological data, which correlates the presence of SARS-CoV-2 ribonucleic acid (RNA) with the number of new daily positive cases officially reported. This last was confirmed via Reverse Transcriptase-quantitative Polymerase Chain Reaction (RT-qPCR) testing which unfortunately is not suitable for real-time surveillance. In addition, WBE concept may act as a faster protocol to alert the public health authorities to take administrative orders (possible re-emerging infections) due to the impracticality of testing all citizens in a short time with limited diagnostic facilities. A comprehensive and integrated review covering all steps starting from sampling to molecular detection of SARS-CoV-2 in wastewater has been made to guide for the development well-defined and reliable protocols.