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

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

CRISPR-Based Assays for Point-of-Need Detection and Subtyping of Influenza

The high disease burden of influenza virus poses a significant threat to human health. Optimized diagnostic technologies that combine speed, sensitivity, and specificity with minimal equipment requirements are urgently needed to detect the many circulating species, subtypes, and variants of influenza at the point of need. Here, we introduce such a method using Streamlined Highlighting of Infections to Navigate Epidemics (SHINE), a clustered regularly interspaced short palindromic repeats (CRISPR)-based RNA detection platform. Four SHINE assays were designed and validated for the detection and differentiation of clinically relevant influenza species (A and B) and subtypes (H1N1 and H3N2). When tested on clinical samples, these optimized assays achieved 100% concordance with quantitative RT-PCR. Duplex Cas12a/Cas13a SHINE assays were also developed to detect two targets simultaneously. This study demonstrates the utility of this duplex assay in discriminating two alleles of an oseltamivir resistance (H275Y) mutation as well as in simultaneously detecting influenza A and human RNAse P in patient samples. These assays have the potential to expand influenza detection outside of clinical laboratories for enhanced influenza diagnosis and surveillance.

A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching

Neonicotinoid insecticide (NEO) residues in agricultural soils have concerning and adverse effects on agroecosystems. Previous studies on the effects of farmland type on NEOs are limited to comparing greenhouses with open fields. On the other hand, both NEOs and microplastics (MPs) are commonly found in agricultural fields, but their co-occurrence characteristics under realistic fields have not been reported. This study grouped farmlands into three types according to the covering degree of the film, collected 391 soil samples in mainland China, and found significant differences in NEO residues in the soils of the three different farmlands, with greenhouse having the highest NEO residue, followed by farmland with film mulching and farmland without film mulching (both open fields). Furthermore, this study found that MPs were significantly and positively correlated with NEOs. As far as we know this is the first report to disclose the association of film mulching and MPs with NEOs under realistic fields. Moreover, multiple linear regression and random forest models were used to comprehensively evaluate the factors influencing NEOs (including climatic, soil, and agricultural indicators). The results indicated that the random forest model was more reliable, with MPs, farmland type, and total nitrogen having higher relative contributions.

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

"Smart markets": harnessing the potential of new technologies for endemic and emerging infectious disease surveillance in traditional food markets

Emerging and endemic zoonotic diseases continue to threaten human and animal health, our social fabric, and the global economy. Zoonoses frequently emerge from congregate interfaces where multiple animal species and humans coexist, including farms and markets. Traditional food markets are widespread across the globe and create an interface where domestic and wild animals interact among themselves and with humans, increasing the risk of pathogen spillover. Despite decades of evidence linking markets to disease outbreaks across the world, there remains a striking lack of pathogen surveillance programs that can relay timely, cost-effective, and actionable information to decision-makers to protect human and animal health. However, the strategic incorporation of environmental surveillance systems in markets coupled with novel pathogen detection strategies can create an early warning system capable of alerting us to the risk of outbreaks before they happen. Here, we explore the concept of "smart" markets that utilize continuous surveillance systems to monitor the emergence of zoonotic pathogens with spillover potential.IMPORTANCEFast detection and rapid intervention are crucial to mitigate risks of pathogen emergence, spillover and spread-every second counts. However, comprehensive, active, longitudinal surveillance systems at high-risk interfaces that provide real-time data for action remain lacking. This paper proposes "smart market" systems harnessing cutting-edge tools and a range of sampling techniques, including wastewater and air collection, multiplex assays, and metagenomic sequencing. Coupled with robust response pathways, these systems could better enable Early Warning and bolster prevention efforts.

Targeted amplicon sequencing facilitated a novel risk assessment framework for assessing the prevalence of broad spectrum bacterial and coronavirus diseases

How to effectively leverage wastewater data to estimate the risk of various infectious diseases remains a great challenge. To address this issue, we conducted continuous wastewater surveillance in Dalian city during the summer-autumn seasons of 2022, targeting coronavirus and bacterial diseases. The surveillance included daily sampling at a wastewater treatment plant (WWTP) and weekly sampling in three sewersheds. Targeting the bacteria's 16S rRNA gene and the coronavirus's RNA-dependent RNA polymerase (RdRp) gene, we first employed RT-PCR and amplicon sequencing techniques to analyze the presence and phylogenetic relationship of detected coronavirus and bacterial pathogens. Next, qPCR was used to quantify the abundances of detected coronavirus and bacterial species. Based on the daily shedding dynamics of SARS-CoV-2, a novel model was developed to predict daily new cases. Based on the medium shedding density of 12 pathogens, two thresholds of sewage pathogen load (indicating 0.1 % and 1 % infection rates) were proposed. Our PanCoV RT-PCR detected coronavirus on 12th August and from 26th August to 12th September 2022. Targeted amplicon sequencing further identified human coronavirus OC43 (hCoV-OC43) on 12th August and the SARS-CoV-2 Omicron variant since 26th August in samples from WWTPs and sewersheds. Phylogenetic analysis revealed that hCoV-OC43 from this study belonged to genotype K and suggested a close relationship between the amplified coronavirus sequences from wastewater and clinical samples in a local COVID-19 outbreak on 26th August. Amplicon sequencing targeting the bacterial 16S rRNA gene also revealed the presence of several bacterial pathogens. Finally, we assessed the microbial risk of specific pathogens in sewersheds and identified a number of pathogens that reached high (>1 % prevalence) and medium risk levels (>0.1 % prevalence) at sewershed B. Our findings underline wastewater surveillance as a valuable early warning system for coronavirus and other waterborne bacterial diseases, complementing public health response measures.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data

BACKGROUND

Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, selecting reference sequences or mutations directly affects the predictive power.

RESULTS

Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage detection, with the marker mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test datasets and illustrate the effects of virus lineage composition of wastewater samples and references.

CONCLUSIONS

Our study highlights current computational challenges, focusing on the general reference design, which directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of defining robust quality metrics.

Impacts of lid closure during toilet flushing and of toilet bowl cleaning on viral contamination of surfaces in United States restrooms

BACKGROUND

Viral aerosols generated during toilet flushing represent a potential route of pathogen transmission. The goal of this study was to determine the impact of toilet lid closure prior to flushing on the generation of viral aerosols and cross-contamination of restroom fomites.

METHODS

A surrogate for human enteric viruses (bacteriophage MS2) was added to household and public toilet bowls and flushed. The resulting viral contamination of the toilet and other restroom surfaces was then determined.

RESULTS

After flushing the inoculated toilets, toilet seat bottoms averaged >107 PFU/100 cm2. Viral contamination of restroom surfaces did not depend on toilet lid position (up or down). After toilet bowls were cleaned using a bowl brush with or without a commercial product (hydrochloric acid), a >4 log10 (>99.99%) reduction in contamination of the toilet bowl water was observed versus no product. Bowl brush contamination was reduced by 1.6 log10 (97.64%) when the product was used versus no product.

CONCLUSIONS

These results demonstrate that closing the toilet lid prior to flushing does not mitigate the risk of contaminating bathroom surfaces and that disinfection of all restroom surfaces (ie, toilet rim, floors) may be necessary after flushing or after toilet brush used for the reduction of virus cross-contamination.

Wastewater-based epidemiology of Campylobacter spp.: A systematic review and meta-analysis of influent, effluent, and removal of wastewater treatment plants

Campylobacter spp. is one of the four leading causes of diarrhoeal diseases worldwide, which are generally mild but can be fatal in children, the elderly, and immunosuppressed persons. The existing disease surveillance for Campylobacter infections is usually based on untimely clinical reports. Wastewater surveillance or wastewater-based epidemiology (WBE) has been developed for the early warning of disease outbreaks and the detection of the emerging new variants of human pathogens, especially after the global pandemic of COVID-19. However, the WBE monitoring of Campylobacter infections in communities is rare due to a few large data gaps. This study is a meta-analysis and systematic review of the prevalence of Campylobacter spp. in various wastewater samples, primarily the influent of wastewater treatment plants. The results showed that the overall prevalence of Campylobacter spp. was 53.26 % in influent wastewater and 52.97 % in all types of wastewater samples. The mean concentration in the influent was 3.31 ± 0.39 log10 gene copies or most probable number (MPN) per 100 mL. The detection method combining culture and PCR yielded the highest positive rate of 90.86 %, while RT-qPCR and qPCR were the two most frequently used quantification methods. In addition, the Campylobacter concentration in influent wastewater showed a seasonal fluctuation, with the highest concentration in the autumn at 3.46 ± 0.41 log10 gene copies or MPN per 100 mL. Based on the isolates of all positive samples, Campylobacter jejuni (62.34 %) was identified as the most prevalent species in wastewater, followed by Campylobacter coli (30.85 %) and Campylobacter lari (4.4 %). These findings provided significant data to further develop and optimize the wastewater surveillance of Campylobacter spp. infections. In addition, large data gaps were found in the decay of Campylobacter spp. in wastewater, indicating insufficient research on the persistence of Campylobacter spp. in wastewater.

Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.

Longitudinal wastewater surveillance of four key pathogens during an unprecedented large-scale COVID-19 outbreak in China facilitated a novel strategy for addressing public health priorities-A proof of concept study

Wastewater-based epidemiology (WBE) is a promising tool for monitoring the spread of SARS-CoV-2 and other pathogens, providing a novel public health strategy to combat disease. In this study, we first analysed nationwide reports of infectious diseases and selected Salmonella, norovirus, and influenza A virus (IAV) as prioritized targets apart from SARS-CoV-2 for wastewater surveillance. Next, the decay rates of Salmonella, norovirus, and IAV in wastewater at various temperatures were established to obtain corrected pathogen concentrations in sewage. We then monitored the concentrations of these pathogens in wastewater treatment plant (WWTP) influents in three cities, establishing a prediction model to estimate the number of infected individuals based on the mass balance between total viral load in sewage and individual viral shedding. From October 2022 to March 2023, we conducted multipathogen wastewater surveillance (MPWS) in a WWTP serving one million people in Xi'an City, monitoring the concentration dynamics of SARS-CoV-2, Salmonella, norovirus, and IAV in sewage. The infection peaks of each pathogen were different, with Salmonella cases and sewage concentration declining from October to December 2022 and only occasionally detected thereafter. The SARS-CoV-2 concentration rapidly increased from December 5th, peaked on December 26th, and then quickly decreased until the end of the study. Norovirus and IAV were detected in wastewater from January to March 2023, peaking in February and March, respectively. We used the prediction models to estimate the rate of SARS-CoV-2 infection in Xi'an city, with nearly 90 % of the population infected in urban regions. There was no significant difference between the predicted and actual number of hospital admissions for IAV. We also accurately predicted the number of norovirus cases relative to the reported cases. Our findings highlight the importance of wastewater surveillance in addressing public health priorities, underscoring the need for a novel workflow that links the prediction results of populations with public health interventions and allocation of medical resources at the community level. This approach would prevent medical resource panic squeezes, reduce the severity and mortality of patients, and enhance overall public health outcomes.

Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

Presence of SARS-CoV-2 virus in wastewater in the Kingdom of Bahrain during the COVID-19 pandemic

Background

Several countries, including Bahrain, used wastewater surveillance for disease activity monitoring. This study aimed to determine the presence of SARS-CoV-2 in untreated wastewater and to correlate it with the disease spread.

Methods

A retrospective review was conducted for all wastewater samples tested for SARS-CoV-2 in public health laboratories from November 2020 to October 2022. Samples were collected weekly between February and October 2022 from different areas across Bahrain. Real-time polymerase chain reaction was used to test for the presence of SARS-CoV-2 in wastewater, and the results were correlated with the number of COVID-19 cases in the same area.

Results

Of 387 wastewater samples, 103 (26.6%) samples tested positive for SARS-CoV-2. In late 2020, of 42 samples collected initially, four (9.5%) samples tested positive for SARS-CoV-2 in the four locations that hosted COVID-19 isolation facilities. Between February and October 2022, 345 specimens of wastewater were tested, and 99 (28.7%) were positive. The highest detection rate was in February, June, and July (60%, 45%, and 43%, respectively), which corresponded to COVID-19 peaks during 2022, and the lowest detection rate was in August and September (11% and 0%, respectively), corresponding to the low number of COVID-19 cases.

Conclusion

The detection rate of SARS-CoV-2 in wastewater samples from Bahrain was high and was significantly correlated with the number of reported COVID-19 cases. Wastewater surveillance can aid the existing surveillance system in monitoring SARS-CoV-2 spread.

Emerging Infectious Diseases Are Virulent Viruses-Are We Prepared? An Overview

The recent pandemic caused by SARS-CoV-2 affected the global population, resulting in a significant loss of lives and global economic deterioration. COVID-19 highlighted the importance of public awareness and science-based decision making, and exposed global vulnerabilities in preparedness and response systems. Emerging and re-emerging viral outbreaks are becoming more frequent due to increased international travel and global warming. These viral outbreaks impose serious public health threats and have transformed national strategies for pandemic preparedness with global economic consequences. At the molecular level, viral mutations and variations are constantly thwarting vaccine efficacy, as well as diagnostic, therapeutic, and prevention strategies. Here, we discuss viral infectious diseases that were epidemic and pandemic, currently available treatments, and surveillance measures, along with their limitations.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

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.

Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice

Infection science is a discipline of healthcare which includes clinical microbiology, public health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures. The importance of infection science has become more apparent in recent years during the SARS-CoV-2 (COVID-19) pandemic and subsequent highlighting of critical operational domains within infection science including the hospital, clinical laboratory, and public health environments to prevent, manage, and treat infectious diseases. However, as the global community transitions beyond the pandemic, the importance of infection science remains, with emerging infectious diseases, bloodstream infections, sepsis, and antimicrobial resistance becoming increasingly significant contributions to the burden of global disease. Machine learning (ML) is frequently applied in healthcare and medical domains, with growing interest in the application of ML techniques to problems in infection science. This has the potential to address several key aspects including improving patient outcomes, optimising workflows in the clinical laboratory, and supporting the management of public health. However, despite promising results, the implementation of ML into clinical practice and workflows is limited. Enabling the migration of ML models from the research to real world environment requires the development of trustworthy ML systems that support the requirements of users, stakeholders, and regulatory agencies. This paper will provide readers with a brief introduction to infection science, outline the principles of trustworthy ML systems, provide examples of the application of these principles in infection science, and propose future directions for moving towards the development of trustworthy ML systems in infection science.

Is SARS-CoV-2 a concern in the largest wastewater treatment plant in middle east?

The surveillance of wastewater treatment plant (WWTP) as the end point of SARS-CoV-2 shed from infected people arise a speculation on transmission of this virus of concern from WWTP in epidemic period. To this end, the present study was developed to comprehensively investigate the presence of SARS-CoV-2 in raw wastewater, effluent and air inhaled by workers and employee in the largest WWTP in Tehran for one-year study period. The monthly raw wastewater, effluent and air samples of WWTP were taken and the SARS-CoV-2 RNA were detected using QIAamp Viral RNA Mini Kit and real-time RT-PCR assay. According to results, the speculation on the presence of SARS-CoV-2 was proved in WWTP by detection this virus in raw wastewater. However, no SARS-CoV-2 was found in both effluent and air of WWTP; this presents the low or no infection for workers and employee in WWTP. Furthermore, further research are needed for detection the SARS-CoV-2 in solid and biomass produced from WWTP processes due to flaks formation, followed by sedimentation in order to better understand the wastewater-based epidemiology and preventive measurement for other epidemics probably encountered in the future.

[Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance]

Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.

In-sewer decay and partitioning of Campylobacter jejuni and Campylobacter coli and implications for their wastewater surveillance

Campylobacter jejuni and coli are two main pathogenic species inducing diarrhoeal diseases in humans, which are responsible for the loss of 33 million lives each year. Current Campylobacter infections are mainly monitored by clinical surveillance which is often limited to individuals seeking treatment, resulting in under-reporting of disease prevalence and untimely indicators of community outbreaks. Wastewater-based epidemiology (WBE) has been developed and employed for the wastewater surveillance of pathogenic viruses and bacteria. Monitoring the temporal changes of pathogen concentration in wastewater allows the early detection of disease outbreaks in a community. However, studies investigating the WBE back-estimation of Campylobacter spp. are rare. Essential factors including the analytical recovery efficiency, the decay rate, the effect of in-sewer transport, and the correlation between the wastewater concentration and the infections in communities are lacking to support wastewater surveillance. This study carried out experiments to investigate the recovery of Campylobacter jejuni and coli from wastewater and the decay under different simulated sewer reactor conditions. It was found that the recovery of Campylobacter spp. from wastewater varied with their concentrations in wastewater and depended on the detection limit of quantification methods. The concentration reduction of Campylobacter. jejuni and coli in sewers followed a two-phase reduction model, and the faster concentration reduction during the first phase is mainly due to their partitioning onto sewer biofilms. The total decay of Campylobacter. jejuni and coli varied in different types of sewer reactors, i.e. rising main vs. gravity sewer. In addition, the sensitivity analysis for WBE back-estimation of Campylobacter suggested that the first-phase decay rate constant (k₁) and the turning time point (t₁) are determining factors and their impacts increased with the hydraulic retention time of wastewater.

Wastewater-based epidemiology for preventing outbreaks and epidemics in Latin America - Lessons from the past and a look to the future

Wastewater-based epidemiology (WBE) is an approach with the potential to complement clinical surveillance systems. Using WBE, it is possible to carry out an early warning of a possible outbreak, monitor spatial and temporal trends of infectious diseases, produce real-time results and generate representative epidemiological information in a territory, especially in areas of social vulnerability. Despite the historical uses of this approach, particularly in the Global Polio Eradication Initiative, and for other pathogens, it was during the COVID-19 pandemic that occurred an exponential increase in environmental surveillance programs for SARS-CoV-2 in wastewater, with many experiences and developments in the field of public health using data for decision making and prioritizing actions to control the pandemic. In Latin America, WBE was applied in heterogeneous contexts and with emphasis on populations that present many socio-environmental inequalities, a condition shared by all Latin American countries. This manuscript addresses the concepts and applications of WBE in public health actions, as well as different experiences in Latin American countries, and discusses a model to implement this surveillance system at the local or national level. We emphasize the need to implement this sentinel surveillance system in countries that want to detect the early entry and spread of new pathogens and monitor outbreaks or epidemics of infectious agents in their territories as a complement of public health surveillance systems.

Occurrence and transport of SARS-CoV-2 in wastewater streams and its detection and remediation by chemical-biological methods

This paper explains the transmission of SARS-CoV and influences of several environmental factors in the transmission process. The article highlighted several methods of collection, sampling and monitoring/estimation as well as surveillance tool for detecting SARS-CoV in wastewater streams. In this context, WBE (Wastewater based epidemiology) is found to be the most effective surveillance tool. Several methods of genomic sequencing are discussed in the paper, which are applied in WBE, like qPCR-based wastewater testing, metagenomics-based analysis, next generation sequencing etc. Additionally, several types of biosensors (colorimetric biosensor, mobile phone-based biosensors, and nanomaterials-based biosensors) showed promising results in sensing SARS-CoV in wastewater. Further, this review paper outlined the gaps in assessing the factors responsible for transmission and challenges in detection and monitoring along with the remediation and disinfection methods of this virus in wastewater. Various methods of disinfection of SARS-CoV-2 in wastewater are discussed (primary, secondary, and tertiary phases) and it is found that a suite of disinfection methods can be used for complete disinfection/removal of the virus. Application of ultraviolet light, ozone and chlorine-based disinfectants are also discussed in the context of treatment methods. This study calls for continuous efforts to gather more information about the virus through continuous monitoring and analyses and to address the existing gaps and identification of the most effective tool/ strategy to prevent SARS-CoV-2 transmission. Wastewater surveillance can be very useful in effective surveillance of future pandemics and epidemics caused by viruses, especially after development of new technologies in detecting and disinfecting viral pathogens more effectively.

Evaluation of population estimation methods for wastewater-based epidemiology in a metropolitan city

This study evaluated the effect of population estimation on the calculation of drug biomarker consumption using wastewater-based epidemiology. Population estimates using mobile phone data, census data, and wastewater quality parameters, such as biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP), were evaluated in six different wastewater treatment plant catchment areas of Busan Metropolitan City, South Korea. The population based on mobile phone data was affected by the patterns of non-resident population movements in each area. The population-normalized daily loads (PNDLs) of methamphetamine were compared according to the different population results. The PNDLs using the population based on mobile phone data (PNDL_Mobile) was 5.87-27.0 mg/d/1000 people. The PNDL_Mobile values were notably different from the PNDLs using wastewater quality parameters (PNDL_Wastewater) (PNDL_Wastewater/PNDL_Mobile: 51-148 %, mean 93 %, relative standard deviation (RSD) 36 %), indicating the unsuitability of population estimation using BOD, TN, and TP. In areas with a large concentration of workplaces, the PNDLs using census data (PNDL_Census) differed from the PNDL_Mobile values (PNDL_Census/PNDL_Mobile: 57-124 %, mean 94 %, RSD 27 %), whereas other areas showed similar values for PNDL_Census and PNDL_Mobile (PNDL_Census/PNDL_Mobile: 95-108 %, mean 102 %, RSD 4.2 %). In particular, the total population estimates of the six survey areas using census data were approximately the same as those based on mobile phone data (RSD: 0.8 %), indicating a decrease in the influence of the non-residential active population in the entire metropolitan city.

COVID-19 and Water Variables: Review and Scientometric Analysis

COVID-19 has changed the world since 2020, and the field of water specifically, boosting scientific productivity (in terms of published articles). This paper focuses on the influence of COVID-19 on scientific productivity with respect to four water variables: (i) wastewater, (ii) renewable water resources, (iii) freshwater withdrawal, and (iv) access to improved and safe drinking water. The field's literature was firstly reviewed, and then the maps were built, emphasizing the strong connections between COVID-19 and water-related variables. A total of 94 countries with publications that assess COVID-19 vs. water were considered and evaluated for how they clustered. The final step of the research shows that, on average, scientific productivity on the water topic was mostly conducted in countries with lower COVID-19 infection rates but higher development levels as represented by gross domestic product (GDP) per capita and the human development index (HDI). According to the statistical analysis, the water-related variables are highly significant, with positive coefficients. This validates that countries with higher water-related values conducted more research on the relationship with COVID-19. Wastewater and freshwater withdrawal had the highest impact on the scientific productivity with respect to COVID-19. Access to safe drinking water becomes insignificant in the presence of the development parameters.

Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era

SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.

Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance

The potential of wastewater-based epidemiology (WBE) as a surveillance and early warning tool for the COVID-19 outbreak has been demonstrated. For areas with limited testing capacity, wastewater surveillance can provide information on the disease dynamic at a community level. A predictive model is a key to generating quantitative estimates of the infected population. Modeling longitudinal wastewater data can be challenging as biomarkers in wastewater are susceptible to variations caused by multiple factors associated with the wastewater matrix and the sewersheds characteristics. As WBE is an emerging trend, the model should be able to address the uncertainties of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great potential. However, most models are built for individual sewersheds with few features extracted from the wastewater. To fulfill the research gap, we compared different time-series and non-time-series models for their short-term predictive performance of COVID-19 cases in 9 diverse sewersheds. The time-series models, long short-term memory (LSTM) and Prophet, outcompeted the non-time-series models. Besides viral (SARS-CoV-2) loads and location identity, domain-specific features like biochemical parameters of wastewater, geographical parameters of the sewersheds, and some socioeconomic parameters of the communities can contribute to the models. With proper feature engineering and hyperparameter tuning, we believe machine learning models like LSTM can be a feasible solution for the COVID-19 trend prediction via WBE. Overall, this is a proof-of-concept study on the application of machine learning in COVID-19 WBE. Future studies are needed to deploy and maintain the model in more real-world applications.

Coordination of SARS-CoV-2 wastewater and clinical testing of university students demonstrates the importance of sampling duration and collection time

Wastewater surveillance has been a useful tool complementing clinical testing during the COVID-19 pandemic. However, transitioning surveillance approaches to small populations, such as dormitories and assisted living facilities poses challenges including difficulties with sample collection and processing. Recently, the need for reliable and timely data has coincided with the need for precise local forecasting of the trajectory of COVID-19. This study compared wastewater and clinical data from the University of Delaware (Fall 2020 and Spring 2021 semesters), and evaluated wastewater collection practices for enhanced virus detection sensitivity. Fecal shedding of SARS-CoV-2 is known to occur in infected individuals. However, shedding concentrations and duration has been shown to vary. Therefore, three shedding periods (14, 21, and 30 days) were presumed and included for analysis of wastewater data. SARS-CoV-2 levels detected in wastewater correlated with clinical virus detection when a positive clinical test result was preceded by fecal shedding of 21 days (p< 0.05) and 30 days (p < 0.05), but not with new cases (p = 0.09) or 14 days of shedding (p = 0.17). Discretely collected wastewater samples were compared with 24-hour composite samples collected at the same site. The discrete samples (n = 99) were composited examining the influence of sampling duration and time of day on SARS-CoV-2 detection. SARS-CoV-2 detection varied among dormitory complexes and sampling durations of 3-hour, 12-hour, and 24-hour (controls). Collection times frequently showing high detection values were between the hours of 03:00 to 05:00 and 23:00 to 08:00. In each of these times of day 33% of samples (3/9) were significantly higher (p < 0.05) than the control sample. The remainder (6/9) of the collection times (3-hour and 12-hour) were not different (p > 0.05) from the control. This study provides additional framework for continued methodology development for microbiological wastewater surveillance as the COVID-19 pandemic progresses and in preparation for future epidemiological efforts.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional "soft" model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Utilization of SARS-CoV-2 Wastewater Surveillance in Africa-A Rapid Review

Wastewater-based epidemiology for SARS-CoV-2 RNA detection in wastewater is desirable for understanding COVID-19 in settings where financial resources and diagnostic facilities for mass individual testing are severely limited. We conducted a rapid review to map research evidence on the utilization of SARS-CoV-2 wastewater surveillance in Africa. We searched PubMed, Google Scholar, and the World Health Organization library databases for relevant reports, reviews, and primary observational studies. Eight studies met the inclusion criteria. Narrative synthesis of the findings from included primary studies revealed the testing methodologies utilized and that detected amount of SARS-CoV-2 viral RNA correlated with the number of new cases in the studied areas. The included reviews revealed the epidemiological significance and environmental risks of SARS-CoV-2 wastewater. Wastewater surveillance data at the community level can be leveraged for the rapid assessment of emerging threats and aid pandemic preparedness. Our rapid review revealed a glaring gap in the primary literature on SARS-CoV-2 wastewater surveillance on the continent, and accelerated and adequate investment into research is urgently needed to address this gap.

Airborne microplastics and SARS-CoV-2 in total suspended particles in the area surrounding the largest medical centre in Latin America

Microplastics (MPs) have been reported in the outdoor/indoor air of urban centres, raising health concerns due to the potential for human exposure. Since aerosols are considered one of the routes of Coronavirus disease 2019 (COVID-19) transmission and may bind to the surface of airborne MPs, we hypothesize that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be associated with the levels of MPs in the air. Our goal was to quantify the SARS-CoV-2 RNA and MPs present in the total suspended particles (TSP) collected in the area surrounding the largest medical centre in Latin America and to elucidate a possible association among weather variables, MPs, and SARS-CoV-2 in the air. TSP were sampled from three outdoor locations in the areas surrounding a medical centre. MPs were quantified and measured under a fluorescence microscope, and their polymeric composition was characterized by Fourier transform infrared (FT-IR) microspectroscopy coupled with attenuated total reflectance (ATR). The viral load of SARS-CoV-2 was quantified by an in-house real-time PCR assay. A generalized linear model (GzLM) was employed to evaluate the effect of the SARS-CoV-2 quantification on MPs and weather variables. TSP samples tested positive for SARS-CoV-2 in 22 out of 38 samples at the three sites. Polyester was the most frequent polymer (80%) found in the samples. The total amount of MPs was positively associated with the quantification of SARS-CoV-2 envelope genes and negatively associated with weather variables (temperature and relative humidity). Our findings show that SARS-CoV-2 aerosols may bind to TSP, such as MPs, and facilitate virus entry into the human body.