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The effective reproductive number, , is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change. We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate in near real time, independent of clinical data and without the associated biases. We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based from this incidence. The method to estimate from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the estimates from case report data as estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer . To our knowledge, this is the first time has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Basic local-alignment search tool (BLAST) is a versatile and commonly used sequence analysis tool in bioinformatics. BLAST permits fast and flexible sequence similarity searches across nucleotide and amino acid sequences, leading to diverse applications such as protein domain identification, orthology searches, and phylogenetic annotation. Most BLAST implementations are command line tools which produce output as comma-separated values files. However, a portable, modular and embeddable implementation of a BLAST-like algorithm, is still missing from our toolbox. Here we present nsearch, a command line tool and C++11 library which provides BLAST-like functionality that can easily be embedded in any application. As an example of this portability we present Blaster which leverages nsearch to provide native BLAST-like functionality for the R programming language, as well as npysearch which provides similar functionality for Python. These packages permit embedding BLAST-like functionality into larger frameworks such as Shiny or Django applications. Benchmarks show that nsearch, npysearch, and Blaster are comparable in speed and accuracy to other commonly used modern BLAST implementations such as VSEARCH and BLAST+. We envision similar implementations of nsearch for other languages commonly used in data science such as Julia to facilitate sequence similarity comparisons. Nsearch, Blaster and npysearch are free to use under the BSD 3.0 license and available on Github Conda, CRAN (Blaster) and PyPi (npysearch).

Background Designing oligonucleotide primers and probes is one of the key steps of various laboratory experiments such as multiplexed PCR or digital multiplexed ligation assays. When designing multiplexed primers and probes to complex, heterogeneous DNA data sets, an optimization problem can arise where the smallest number of oligonucleotides covering the largest diversity of the input dataset needs to be identified. Tools that provide this optimization in an efficient manner for large input data are currently lacking. Results Here we present Prider, an R package for designing primers and probes with a nearly optimal coverage for complex and large sequence sets. Prider initially prepares a full primer coverage of the input sequences, the complexity of which is subsequently reduced by removing components of high redundancy or narrow coverage. The primers from the resulting near-optimal coverage are easily accessible as data frames and their coverage across the input sequences can be visualised as heatmaps using Prider’s plotting function. Prider permits efficient design of primers to large DNA datasets by scaling linearly to increasing sequence data, regardless of the diversity of the dataset. Conclusions Prider solves a recalcitrant problem in molecular diagnostics: how to cover a maximal sequence diversity with a minimal number of oligonucleotide primers or probes. The combination of Prider with highly scalable molecular quantification techniques will permit an unprecedented molecular screening capability with immediate applicability in fields such as clinical microbiology, epidemic virus surveillance or antimicrobial resistance surveillance.

Antibiotic resistance is a leading cause of hospitalization and death worldwide. Heavy metals such as arsenic have been shown to drive co-selection of antibiotic resistance, suggesting arsenic-contaminated drinking water is a risk factor for antibiotic resistance carriage. This study aimed to determine the prevalence and abundance of antibiotic-resistant Escherichia coli (AR-Ec) among people and drinking water in high (Hajiganj, >100 μg/L) and low arsenic-contaminated (Matlab, <20 μg/L) areas in Bangladesh. Drinking water and stool from mothers and their children (<1 year) were collected from 50 households per area. AR-Ec was detected via selective culture plating and isolates were tested for antibiotic resistance, arsenic resistance, and diarrheagenic genes by PCR. Whole-genome sequencing (WGS) analysis was done for 30 E . coli isolates from 10 households. Prevalence of AR-Ec was significantly higher in water in Hajiganj (48%) compared to water in Matlab (22%, p <0.05) and among children in Hajiganj (94%) compared to children in Matlab (76%, p <0.05), but not among mothers. A significantly higher proportion of E . coli isolates from Hajiganj were multidrug-resistant (83%) compared to isolates from Matlab (71%, p <0.05). Co-resistance to arsenic and multiple antibiotics (MAR index >0.2) was observed in a higher proportion of water (78%) and child stool (100%) isolates in Hajiganj than in water (57%) and children (89%) in Matlab ( p <0.05). The odds of arsenic-resistant bacteria being resistant to third-generation cephalosporin antibiotics were higher compared to arsenic-sensitive bacteria (odds ratios, OR 1.2–7.0, p <0.01). WGS-based phylogenetic analysis of E . coli isolates did not reveal any clustering based on arsenic exposure and no significant difference in resistome was found among the isolates between the two areas. The positive association detected between arsenic exposure and antibiotic resistance carriage among children in arsenic-affected areas in Bangladesh is an important public health concern that warrants redoubling efforts to reduce arsenic exposure.

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK. The bioinformatics method COJAC enables improved population-level surveillance of the emergence and spread of SARS-CoV-2 variants in wastewater.

Antimicrobial resistance (AMR) is a growing public health challenge that is expected to disproportionately burden lower- and middle-income countries (LMICs) in the coming decades. Although the contributions of human and veterinary antibiotic misuse to this crisis are well-recognized, environmental transmission (via water, soil or food contaminated with human and animal faeces) has been given less attention as a global driver of AMR, especially in urban informal settlements in LMICs—commonly known as ‘shanty towns’ or ‘slums’. These settlements may be unique hotspots for environmental AMR transmission given: (1) the high density of humans, livestock and vermin living in close proximity; (2) frequent antibiotic misuse; and (3) insufficient drinking water, drainage and sanitation infrastructure. Here, we highlight the need for strategies to disrupt environmental AMR transmission in urban informal settlements. We propose that water and waste infrastructure improvements tailored to these settings should be evaluated for their effectiveness in limiting environmental AMR dissemination, lowering the community-level burden of antimicrobial-resistant infections and preventing antibiotic misuse. We also suggest that additional research is directed towards developing economic and legal incentives for evaluating and implementing water and waste infrastructure in these settings. Given that almost 90% of urban population growth will occur in regions predicted to be most burdened by the AMR crisis, there is an urgent need to build effective, evidence-based policies that could influence massive investments in the built urban environment in LMICs over the next few decades. Urban informal settlements, more commonly known as slums, are hotspots for the environmental transmission of antimicrobial resistance (AMR). Here, the authors discuss the behavioural, environmental and structural reasons for this and propose that improvements in water and waste infrastructure, as well as legal and economic incentives, could limit environmental AMR dissemination.

The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the selection advantage of a viral variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are shedding profiles, which can affect the relationship between true viral incidence and viral loads in wastewater. Changes in shedding between variants may decouple the established relationship between wastewater loads and clinical test data. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that estimates of the selection advantage of a variant are not biased by shedding profiles. We show that they are robust to differences in shedding between variants under a wide range of assumptions, and identify specific conditions under which this robustness may break down. Additionally, we demonstrate that differences in shedding only briefly affect estimates of the effective reproduction number. Thus, estimates of selective advantage and reproduction numbers derived from wastewater maintain their advantages over traditional clinical data, even when there are differences in shedding among variants. Epidemiological estimates from wastewater are not biased by testing rates but may be subject to other biases. Here, the authors investigate the impact of variable virus shedding profiles for different SARS-CoV-2 variants on estimates of their selection advantage.

The frequent contact people have with liquids containing pathogenic microorganisms provides opportunities for disease transmission. In this work, we quantified the transfer of bacteria-using E. coli as a model- from liquid to skin, estimated liquid retention on the skin after different contact activities (hand immersion, wet-cloth and wet-surface contact), and estimated liquid transfer following hand-to-mouth contacts. The results of our study show that the number of E. coli transferred to the skin per surface area (n [E. coli/cm.sup.2 ]) can be modeled using n = C (10.sup.-3.38 +h), where C [E. coli/cm.sup.3 ] is the concentration of E. coli in the liquid, and h [cm] is the film thickness of the liquid retained on the skin. Findings from the E. coli transfer experiments reveal a significant difference between the transfer of E. coli from liquid to the skin and the previously reported transfer of viruses to the skin. Additionally, our results demonstrate that the time elapsed since the interaction significantly influences liquid retention, therefore modulating the risks associated with human interaction with contaminated liquids. The findings enhance our understanding of liquid-mediated disease transmission processes and provide quantitative estimates as inputs for microbial risk assessments.

As more countries engage in water reuse, either intended or de facto, there is an urgent need to more comprehensively evaluate resulting environmental and public health concerns. While antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) are increasingly coming under the spotlight, as emerging contaminants, existing water reuse regulations and guidelines do not adequately address these concerns. This perspectives paper seeks to frame the various challenges that need to be resolved to identify meaningful and realistic target types and levels of antibiotic resistance benchmarks for water reuse. First, there is the need for standardized and agreed-upon methodologies to identify and quantify ARB and ARGs. Second, even if methodologies are available, identifying which ARB and ARGs to monitor that would best relate to the occurrence of disease burden remains unknown. Third, a framework tailored to assessing the risks associated with ARB and ARGs during reuse is urgently needed. Fourth, similar to protecting drinking water sources, strategies to prevent dissemination of ARB and ARGs via wastewater treatment and reuse are required to ensure that appropriate barriers are emplaced. Finally, current wastewater treatment technologies could benefit from modification or retrofit to more effectively remove ARB and ARGs while also producing a high quality product for water and resource recovery. This perspectives paper highlights the need to consider ARB and ARGs when evaluating the overall safety aspects of water reuse and ways by which this may be accomplished.

Antimicrobial resistance (AMR) is a global health threat and is commonly monitored in clinical settings, given its association with the risk of antimicrobial-resistant infections. Nevertheless, tracking AMR within a community proves challenging due to the substantial sample size required for a representative population, along with high associated costs and privacy concerns. By investigating high resolution temporal and geographic trends in extended-spectrum beta-lactamase producing Escherichia coli in wastewater, we provide an alternative approach to monitor AMR dynamics, distinct from the conventional clinical settings focus. Through this approach, we develop a mechanistic model, shedding light on the relationship between wastewater indicators and AMR carriage in the population. This perspective contributes valuable insights into trends of AMR carriage, emphasizing the importance of wastewater surveillance in informing effective public health interventions.