Environmental Science & Technology Letters

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicological risks that standard additive models fail to capture. This study elucidates a novel "metabolic blockade" mechanism wherein PFAS competitively inhibits the renal excretion of VOC metabolites, thereby amplifying neurotoxic burdens. Utilizing a Double Machine Learning (DML) framework on data from National Health and Nutrition Examination Survey (2005-2020), we analyzed a final intersectional cohort of 1,975 participants. We identified a robust inhibition of VOC metabolite clearance by serum PFAS. Specifically, PFNA significantly suppressed the excretion of the benzene metabolite URXPMA (Causal {beta}TMLE = -0.219, p < 0.001), with efficacy dependent on perfluorinated chain length. Molecular docking simulations revealed the biophysical basis of this antagonism: long-chain PFNA exhibited superior binding affinity to the Organic Anion Transporter 1 (OAT1) ({Delta}G = -6.333 kcal/mol) compared to native VOC metabolites ({Delta}G = -4.957 kcal/mol), confirming high-affinity competitive inhibition at the renal interface. In a neurocognitive sub-cohort (N = 1,200), this interference translated into functional synergism; high-PFNA exposure magnified VOC-associated cognitive impairment by 1.5-fold and significantly exacerbated the negative association between VOC burden and processing speed ({beta}int = -0.263, p = 0.004). These findings define PFAS as a "metabolic amplifier" of co-contaminant toxicity, necessitating a paradigm shift toward mixture-based hazardous material regulations that account for transporter-level interactions.

We present findings from the first known pilot study of transatlantic airplane wastewater monitoring, conducted over six months at two connected international airports in the United States and the United Kingdom. This study demonstrates the feasibility of implementing bilateral wastewater-based pathogen surveillance at international travel hubs. We outline the operational and analytical methodologies employed, highlight key challenges encountered in transnational coordination, and provide recommendations for the design and implementation of future surveillance programs at points of entry.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes. ImportanceAntimicrobial resistance (AMR) threatens modern medicine. Water reclamation facilities receive a complex mixture of antibiotics and rely on active microbial communities for treatment, thereby acting as critical systems to prevent environmental spread of resistance. However, AMR dynamics are difficult to discern in complex wastewater environments due to antibiotic resistance genes (ARGs) being frequently carried on mobile pieces of DNA that are difficult to link to specific bacteria using conventional shotgun sequencing. Novel proximity ligation sample preparation techniques like Hi-C physically link co-located sequences of DNA before shotgun sequencing. This allows sequencing to elucidate the bacterial hosts for both stable and mobile ARGs. In the current study, Hi-C sequencing was carried out on influent, activated sludge, and effluent collected from water reclamation facilities in California, Texas, and North Carolina to assess the resistome host range across treatment. 5 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/26346186v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e4620eorg.highwire.dtl.DTLVardef@e1c3a7org.highwire.dtl.DTLVardef@1f40964org.highwire.dtl.DTLVardef@94b886_HPS_FORMAT_FIGEXP M_FIG C_FIG

Antimicrobial resistance (AMR) in ESKAPE pathogens represents a major global health threat. Although these organisms are well established as causes of healthcare-associated infections, aquatic environments may function as reservoirs and transmission pathways for resistance. This systematic review aimed to estimate the prevalence of AMR in ESKAPE pathogens isolated from water and wastewater and to compare resistance patterns with those observed in human clinical isolates. The review followed PRISMA guidelines and was registered in PROSPERO (CRD420251020930). PubMed, Embase, and the Cochrane Library were searched to January 14, 2025. Eligible studies were original research reporting antimicrobial susceptibility data for ESKAPE pathogens isolated from both aquatic environmental matrices and clinical samples. Pooled resistance prevalence was estimated using generalized linear mixed models, with heterogeneity assessed using {tau}{superscript 2} and I{superscript 2} statistics and small-study effects evaluated by funnel plots and Eggers test. Of 304 records identified, 18 studies met the inclusion criteria. The pooled overall resistance prevalence was 0.46 (95% CI: 0.36-0.57), with heterogeneity (I{superscript 2} = 98.8%). Resistance was higher in clinical isolates (0.67; 95% CI: 0.55-0.77) than in environmental isolates (0.24; 95% CI: 0.14-0.39), and environmental resistance was greater in effluent-impacted waters than in non-effluent sources. Interpretation is limited by methodological heterogeneity, selective isolation approaches in environmental studies, and imprecision due to small and unevenly distributed samples. Overall, AMR in ESKAPE pathogens remains more prevalent in clinical settings, but aquatic environments, particularly wastewater, represent resistance reservoirs, underscoring the need for standardized methodologies within a One Health framework. Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251020930, CRD420251020930 HighlightsAntimicrobial resistance was higher in clinical isolates than in aquatic isolates. Resistance patterns showed extreme heterogeneity across studies. Effluent-impacted waters showed higher resistance than non-effluent sources. Higher environmental resistance in some classes reflected methodological artifacts.

Measles outbreaks have surged globally in recent years, but current surveillance systems have limited capacity to monitor measles virus (MeV) transmission and evolution at population scale. Although MeV can be detected in wastewater, the public health potential of wastewater genomic surveillance for MeV remains largely unexplored. Here, we deploy sensitive, low-cost MeV wastewater genomic surveillance combining virus concentration, whole-genome amplicon sequencing, and bioinformatic analysis alongside routine clinical genomic surveillance during the 2024-25 outbreak in South Africa. Integrated phylogenetic analyses of wastewater and clinical MeV genomes revealed previously undetected interprovincial spread and transmission links not captured by standard N450 sequencing. Our findings demonstrate that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control and elimination efforts.

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

Accurate quantification of individual exposure to air pollutants remains a major challenge in environmental health, as fixed-site monitoring fails to account for mobility, indoor environments, and physiological variability. We deployed TracMyAir, a smartphone-based digital health platform designed to generate time-resolved, personalized exposure and inhaled dose estimates for PM2.5 and ozone under real-world conditions. In an exploratory study of 18 adults contributing more than 1,500 participant-hours, the platform integrated smartphone geolocation, regulatory (AirNow) and community-based (PurpleAir) air quality data, building infiltration modeling, microenvironment classification, and wearable-derived physical activity metrics to compute eight tiers of hourly exposure estimates, culminating in individualized inhaled dose. Hourly dose estimates derived from smartphone-and smartwatch-based step counts were concordant (Spearman correlation p=0.97-0.98), while heart rate-based estimates yielded greater variability and higher mean values (p=0.82-0.92). Exposure explained 51-73% of variance in inhaled dose of PM2.5 and 68-84% of ozone, suggesting that physiological-based modeling approaches improve hyperlocal estimates of personal pollutant burden. Substantial inter-and intra-individual variability reflect dynamic microenvironmental transitions and activity patterns. Modeled doses based on regulatory and community sensor networks were strongly correlated (R=0.84), with community sensors located closer to participants on average, supporting the feasibility of integrating dense, low-cost monitoring networks. No consistent association was observed between outdoor pollutant levels and neighborhood socioeconomic status in this cohort. These findings demonstrate the feasibility of a scalable, smartphone-centered digital health approach for hyperlocal exposure and inhaled dose modeling. By leveraging ubiquitous consumer devices and existing air quality networks, TracMyAir enables personalized environmental exposure assessment with potential applications in epidemiology, population health, and precision environmental medicine.

Bacterial infections are a major cause of morbidity and mortality among children under five in low- and middle-income countries (LMICs). Children in LMICs are exposed to and colonized by a range of pathogenic bacteria, yet patterns of bacterial exchange between humans are not well known, in part because culturing and sequencing single bacterial isolates is labor-intensive. Here, we apply a machine learning strain tracking approach to metagenomic data from 511 stool samples from children and mothers across urban and rural Kenyan communities to characterize bacterial dissemination and assess if community-wide water chlorination disrupts transmission. We identified distinct strain-sharing dynamics across species; potentially pathogenic taxa (e.g., Escherichia, Enterococcus, Campylobacter) exhibited distance-dependent dissemination driven by young children, while commensal taxa (e.g., Bifidobacterium, Bacteroides) showed patterns consistent with dietary exposure. Drinking water chlorination reduced community-level strain-sharing in rural communities. Our study provides the first strain-level insights into multi-species bacterial transmission dynamics in LMIC communities, identifying distinct dissemination pathways for facultative versus mostly anaerobic bacteria. Moreover, our findings highlight the utility of metagenomic strain tracking to uncover how community spread can be disrupted.

Hepatitis E virus (HEV) is considered a predominantly foodborne pathogen in developed settings. During COVID-19 lockdown periods, however, HEV concentrations in wastewater at a treatment plant in Munich, Germany decreased, suggesting that pandemic-related behaviour changes inadvertently influenced transmission. In contrast, reported cases and wastewater data from a smaller catchment showed no comparable decline. To assess whether the observed reduction is compatible with a near-exclusively foodborne infection and to reconcile the contrasting signals across surveillance modalities, we developed a stochastic, individual-level model of HEV transmission, shedding, and ascertainment in Munich. Using Approximate Bayesian Computation, we calibrated this model to wastewater and case data from 2020-2023, first separately and then jointly. Posterior parameter estimates indicated a substantial decline in transmission during lockdowns to about 35-40% of the non-lockdown level, with the 95% credible interval entirely below 1 (no change). Joint inference suggested that possible modest lockdown-associated increases in diagnosis probabilities and higher measurement variability in the smaller catchment masked this effect in clinical and small-scale wastewater data, respectively. These findings demonstrate how wastewater-based surveillance, used alongside reported cases, can enable more robust parameter inference for models of under-reported pathogens like HEV, thereby supporting informed public health risk assessments.

Abstract This protocol is reported in accordance with the SPIRIT 2025 guidelines for clinical trial protocols. Introduction: Young children, from birth to age 5 y are particularly vulnerable to indoor air pollutants and respiratory pathogens. Portable air purifiers (or filtration) and upper-room ultraviolet germicidal irradiation (UVGI) are two widely used interventions with the potential to improve indoor air quality (IAQ) and reduce sick-related absences. However, a review of the literature revealed no real-world randomized studies evaluating their effectiveness in reducing young children's sick-related absences in early care and education (ECE) classrooms. Methods and Analysis: The OK-AIR study is a longitudinal, cluster-randomized 2x2 factorial trial conducted in Head Start centers using two implementation cohorts: Cohort 1 (five Head Start centers and 20 classrooms from 2023 to 2024) and Cohort 2 (11 centers and 59 classrooms from 2025 to 2026), with expanded inclusion of rural areas. Cohort 1 enrolled 204 children, 48 teachers and 5 site directors, and Cohort 2 enrolled 462 children, 97 teachers and 11 site directors. Within each center, four classrooms are randomized to: (1) control; (2) portable filtration; (3) upper-room ultraviolet germicidal irradiation (UVGI); or (4) both interventions. Cohort 2 was initially planned as a second factorial trial but was amended to a purifier-only design due to funding changes; details are provided in the protocol amendments section. We collect continuous IAQ data, including particulate matter (PM) with aerodynamic diameters [&le;]1 m (PM1), [&le;]2.5 m (PM2.5), [&le;]4 m (PM4), and [&le;]10 m (PM10); total volatile organic compounds (TVOCs) index; nitrogen oxides (NOx) index; carbon monoxide (CO), noise; temperature; and relative humidity, alongside daily child absences. Seasonal environmental surface swabs (dining tables and toilet flooring) are tested by Reverse-Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR) for Influenza A/B, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus Type 3 (HPIV3), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Norovirus. IAQ monitoring is structured across Winter, Spring, Summer, and Fall, including designated baseline/off-period weeks to characterize temporal and seasonal variability in environmental measures across classrooms and centers. Multi-informant surveys (Director, Teacher, Parent) capture contextual factors, and children's social-emotional development is assessed using teacher ratings on the Devereux Early Childhood Assessment (DECA). The primary outcome is the sick-related absence rate, analyzed as cumulative absences over the attendance year while accounting for clustering by school and classroom using generalized mixed-effects models. Secondary outcomes include children's social-emotional ratings, IAQ metrics and pathogen detection rates; analyses of IAQ incorporate time/seasonal structure, and season-stratified absenteeism analyses will be treated as secondary/exploratory refinements. An economic evaluation will estimate incremental intervention costs and cost-effectiveness/cost-benefit (such as cost per sick-related absence day averted). Ethics and Dissemination: This study was approved by the Institutional Review Board (IRB) at the University of Oklahoma. Findings will be shared through peer-reviewed publications; presentations at local, state, and national conferences; research briefs developed for lay and policy audiences; and community briefings prioritizing the participating early childhood programs and communities. ISRCTN Trial Registration: ISRCTN78764448 Disclaimer: The views expressed are those of the authors and do not reflect the official views of the Uniformed Services University or the United States Department of War. Strengths and Limitations of This Study: {middle dot} Real-world longitudinal cluster RCT: The study uses a rigorous longitudinal cluster-randomized 2x2 factorial design in real-world ECE settings. {middle dot} Combined interventions: Interventions target both air filtration and disinfection, allowing for combined and comparative evaluation. {middle dot} Objective air quality monitoring: Continuous monitoring of IAQ metrics provides objective and reliable data on environmental change. {middle dot} Environmental pathogen surveillance: qPCR on surface swabs yields an objective biological outcome to triangulate with IAQ and absences. {middle dot} Comprehensive context and child measures: Multi-method and multi-reporter data collection includes Head Start attendance records, continuous air monitoring, pathogen detection, contextual surveys completed by center directors, teachers, and parents, and standardized social-emotional assessments (DECA) completed by classroom teachers. Head Start program records providing children's longer-term health data available through Health Insurance Portability and Accountability Act (HIPAA) authorization. {middle dot} Clustered/temporal complexity: Seasonal design accounts for variation over time but may introduce complexity in modeling temporal effects. {middle dot} Practical Implications: Study findings will have practical implications for Head Start and other ECE programs striving to maximize child attendance with cost effective strategies. Keywords: Early childhood; Head Start; indoor air quality (IAQ); air purifiers; filtration; ultraviolet germicidal irradiation; cluster randomized trial; absenteeism; environmental pathogens; DECA; cost-benefit analysis

Increasing human mobility and population connectivity have intensified the risks of global pathogen spread, while concurrent shifts in human demographic patterns, ecological factors, and climatic conditions have altered the global landscape of this risk. Genomic surveillance can serve as a critical tool for early detection of emerging pathogen threats; however, challenges remain in deciding where to monitor, in understanding trade-offs among surveillance modalities, and in translating detections into actionable estimates of importation and local transmission for public health decision-making. Here we develop a computational framework to evaluate strategies for respiratory pathogen detection that integrates an established clinical surveillance modality, intensive care unit (ICU) sampling, with an emerging environmental modality, aircraft wastewater (AWW) sampling. Detections are translated into risk via a multi-scale, stochastic global transmission model that combines international flight data with a detailed agent-based local transmission model. The resulting model-based estimates contrast the time to pathogen detection via AWW at airports with that in the community via realistic healthcare testing pathways. Using real-world data from England and Wales (EW), we find that employing AWW in EW airports can improve first detection times by 12.5-37.7 days for a range of epidemiological parameters under realistic healthcare testing scenarios and random aircraft sampling between 25 and 50%. In particular, for a SARS-CoV-2-like pathogen, we expect AWW to outperform ICU in first detection timing by 22.0-25.6 days, with [~]21.9-42.6 times fewer cases at their respective time of detection. While false detection remains a risk, we show that follow-up confirmatory testing can improve detection confidence substantially. Together our results demonstrate the potential utility of AWW surveillance and how it can reduce detection times and improve global health security.

Food costs are more significantly impacted by climate change as countries grow. It is well known that climate change has an impact on the productivity of most agricultural goods, but it is unclear how specifically it will affect food costs. The present research explores how the North Atlantic Oscillation (NAO) index, a widely used climate indicator, affects food prices around the world. This is achieved by applying a robust bivariate Hurst exponent (robust bHe). The research creates a color map of this coefficient using a window-sliding technique over various intervals of time, displaying an illustration that changes overtime. Additionally, the NAO index and global food prices are examined for causal connections using variable-lag transfer entropy using a window-sliding technique. The results show that notable rises in a number of international food prices for long as well as short periods are associated with significant increases in the NAO index. Furthermore, the causative function of the NAO index in influencing global food costs is confirmed by variable-lag transfer entropy. Is highly recommended as it directly connects the research to actionable outcomes for policymakers and the overarching goal of sustainability and food security. This study provides the first direct evidence of a robust, long-range cross-correlation and causal link between the North Atlantic Oscillation (NAO) index and key global food prices. It introduces a novel, robust methodological framework to visualize this time-varying relationship, offering a critical tool for policymakers and forecasting models.

Despite the relevance of spatial mapping in analyzing the health situation and understanding the risk factors and determinants of leptospirosis, peripheral urban communities often remain invisible on maps, which tend to use data and methods that do not express community contribution nor promote local participation. Furthermore, in the implementation of sanitation interventions, the same happens: there is limited user participation, and a lack of identification of intervention needs based on the perception of community residents, failing the interventions. We conducted a cross-sectional study through collaborative mapping from February to October 2022 with 213 residents and self-declared heads-of-household in two peripheral urban communities. We analyzed the perception of sanitation needs indicated by residents and their relationship with the risk of leptospirosis in these communities. Based on community perception, sewage (NS: 87.1%; JSI/ME: 84.9%) and urban cleaning and solid waste management (NS: 25.9%; JSI/ME: 32.6%) were the sanitation needs. In NS, most participants indicated that the necessary interventions for sewage improvement were actions of sewer cleaning and sealing (26.5%), sewer cleaning and piping (23.5%), and implementation/installation/construction of a sanitary sewage network (41.4%). In JSI/ME, interventions included sewage sealing (48.7%) and piping (25.6%), in addition to actions to maintain sewage cleaning (93.3%). The removal of solid waste (trash) in the square (NS: 22.2%) and on the streets (JSI/ME: 69.2%), as well as community awareness (JSI/ME: 15.4%), were indicated as interventions to meet the needs of urban cleaning and solid waste management. Respondents agreed on where interventions should occur, which congregated around the local river. We found a negative correlation between the predicted leptospirosis seropositivity and perceived intervention needs in both study areas. The prevention of diseases such as leptospirosis in peripheral urban communities requires integrated basic sanitation interventions, encompassing different components and aligned with the local needs perceived by residents.

Mycobacterium tuberculosis (MTB) disease is a major global health threat with most tuberculosis (TB) cases occurring in low-and middle-income countries (LMIC) with limited healthcare infrastructure. Near-point-of-care testing which can be deployed at peripheral clinical settings is needed to start treatment earlier and thereby improve treatment outcomes. Here we report the development and preliminary characterization of an MTB detection assay that utilizes tongue swab or sputum specimens for The DASH(R) Rapid PCR System which employs cartridge-based automated sequence specific capture sample prep combined with dual target qPCR multicopy MTB insertion sequences IS6110 and IS1081 amplification and detection. MTB is resistant to conventional bacterial lysis techniques; therefore, we evaluated two pre-cartridge lysing techniques, mechanical lysis and sonication, and selected sonication for all subsequent studies. The DASH MTB assay demonstrated a limit of detection of 2.5 MTB cells/swab with no detection of 10 non-tuberculosis Mycobacterium strains. Clinical testing of 100 (49 positive and 51 negative) de-identified blinded sputa from South African symptomatic clinic attendees yielded an overall test sensitivity of 96% (100% for smear positive samples and 88% for smear negative samples) and specificity of 88% when compared to sputum culture. In a separate study of 110 tongue swab specimens (70 positive and 40 negative) from South African symptomatic clinic attendees, the sensitivity was 93% and the specificity was 100%. We further demonstrated that the test is compatible with peripheral LMIC settings via external battery operation and cartridge stability at 45{degrees}C for up to one year. ImportanceTuberculosis (TB) is the single most deadly infectious disease with 1.23 million deaths in 2024. Near-point-of-care testing which can be deployed at peripheral settings that lack laboratory infrastructure to deliver prompt and accurate diagnosis is needed to start treatment earlier and thereby improve treatment outcomes. In this study, we have developed an automated test to detect Mycobacterium tuberculosis (MTB), the cause of TB, from sputum and tongue swab specimens. Its high sensitivity and specificity, rapid time to result, and compatibility with environments that lack air conditioning and consistent electricity make this assay suitable for diverse clinical settings.

BackgroundDengue outbreaks have become a severe threat to Bangladesh as the infections and mortality numbers are skyrocketing in recent years. Favorable environmental and anthropogenic conditions have established the capital of Bangladesh, Dhaka city as the epicenter of dengue outbreak. Studies have showed that climate change induced extreme weather events are exacerbating Aedes mosquito breeding and dengue virus transmission conditions. Methodology/Principal FindingsIn this study, short-term (0-6 weeks) associations of maximum temperature and heatwave days on dengue cases in Dhaka city were examined through Distributed Lag Non-linear Model (DLNM) methodology for weekly measurement of 2016-2024, taking into account relative humidity, cumulative rainfall, seasonality and hospital closure effect. Two separate negative binomial models were constructed. The maximum temperature model rendered an overall inverted U-shaped association, where the maximum temperature range of 31.5-33.2{degrees}C showed a sustained elevated dengue risk, with highest risk estimate at 33.2{degrees}C [relative risk (RR): 1.186, 95% CI: 1.002, 1.403]. Whereas, results of weekly heatwave days showed an overall protective effect (RR<1) for dengue cases. The lowest risk of infection was found at 3 heatwave days per week, with RR 0.275 (95% CI: 0.178, 0.423). Multiple sensitivity analyses were conducted for both models to evaluate their robustness. Lastly, the optimized models were analyzed under three distinct sub-periods, to capture the association of exposure variables with predominant circulating serotypes. Conclusions/SignificanceThe findings of the study aim to support public health policymakers and healthcare authorities in designing and implementing effective vector control interventions under emerging climatic emergencies. Author SummaryDengue disease is one of the most buringing issue in Bangladesh in recent years. This vector-borne disease is inherently influenced by climatic variables, i.e., temperature, rainfall, humidity, etc. Moreover, these relations are complex and non-linearly associated. Due to shift in climatic conditions, the occurance of extreme weather events are becoming frequent, with increased magnitude and longer duration. In this study, the nonlinear and delayed association of dengue infections due to the exposure of extreme temperature events were assessed in climate-change vulnerable Dhaka city. To do this, a statistical method was used, called distributed lag nonlinear methodology (DLNM). The results showed that dengue infections had an inverted U-shaped (parabolic) relationship with maximum temperature, while compared to mean maximum temperature, and a suppressive association with heatwaves relative to days without heatwaves. The findings aim to work as an early warning system, and support to policymakes and healthcare authorities to tackle the dengue surge in the changing climate.

Frequent blood testing is a routine but burdensome reality for many children, particularly those with chronic, rare, or medically complex conditions. Repeated clinic, hospital, and laboratory visits can disrupt family life, increase stress for children and caregivers, and limit access to timely monitoring and research participation. Despite advances in pediatric care, blood collection has remained largely tethered to in-person clinical settings. This study validates a new model: safe, effective, parent-administered pediatric blood collection performed at-home. We evaluated the RedDrop ONE capillary blood collection device in a real-world, parent-administered home setting to determine whether non-clinical caregivers can reliably collect clinically meaningful blood samples from children without venipuncture, specialized training, or in-clinic support. Conducted under Institutional Review Board (IRB) oversight, this observational usability study enrolled 50 children aged 3-17 years across a geographically diverse U.S.-based pediatric population, including healthy and medically fragile children with chronic autoimmune and rare diseases. All study activities, including enrollment, consent, instruction, collection, and sample return, were completed remotely, reflecting real-world adoption conditions rather than controlled clinical environments. Parents successfully collected blood samples from their children at home with high consistency, low perceived pain, and strong overall acceptance. Across collections, blood and serum volumes were sufficient and reproducible, and laboratory analysis confirmed strong analytical concordance between samples collected from two different anatomical sites, arm and leg. Parents reported high confidence using the device, short collection times, and a high likelihood of completing collections on the first attempt. Importantly, both parents and children rated the overall experience as better than expected, and parents consistently reported that the RedDrop ONE experience was superior to traditional finger-prick and needle-based venous blood draws. Parents reported minimal child discomfort and greater flexibility by avoiding in-clinic phlebotomy visits. These benefits are especially meaningful for families managing chronic or rare pediatric conditions that require repeated blood monitoring. By enabling blood collection at-home, this model reduces travel burden, scheduling constraints, and procedural anxiety while maintaining analytical reliability. This study also demonstrated that parent-administered pediatric blood collection can support real-world clinical workflows beyond research. All samples were successfully shipped overnight at ambient temperature and processed by a CLIA-certified laboratory, supporting feasibility for remote pediatric patient monitoring and decentralized clinical trials. While lipid testing served as the representative clinical use case, the volumes and consistency achieved exceeded volume thresholds commonly required for advanced downstream applications, including proteomics, metabolomics, transcriptomics, and genomic analyses. Taken together, these findings validate parent-administered pediatric blood collection as a practical, scalable alternative to in-clinic phlebotomy for many use cases. By shifting blood collection from the clinic to the home, this approach has the potential to reduce reliance on in-person phlebotomy, integrate seamlessly into routine pediatric care, and expand access to monitoring and research for families who face geographic, logistical, or medical barriers. For health systems, researchers, and parents alike, this study supports a future in which clinically meaningful pediatric blood collection is no longer limited by healthcare facility location but instead centered on the child and family.

BackgroundFood systems--particularly livestock production--account for substantial greenhouse gas (GHG) emissions, while unhealthy diets, characterized by excessive animal-based and insufficient plant-based food consumption, are a major risk factor for all-cause mortality in Europe. Implementing climate mitigation policies related to the GHG emissions of the food system could therefore bring important health co-benefits. MethodsWe developed a health impact assessment model based on a life table approach and evaluated the mortality impact of transitions in food consumption through four contrasting scenarios leading to net-zero GHG emissions for France in 2050. These involved varying dietary shifts, all moving toward more plant-based foods. For each scenario, we modeled the evolution of the diet, as well as the impacts on all-cause mortality by applying the most recent and robust dose-response relationships derived from meta-analyses for 13 food groups. FindingsThe different trajectories of dietary shifts translated into a health impact ranging from 19% [uncertainty interval, UI: 17%-21%] to 24% [UI: 21%-26%] of all-cause mortality prevented in 2050 in the French population. Variation in intakes of nuts, red meat, processed meat, whole grains and legumes bring most of the health benefits. Whatever the parameters chosen in the sensitivity analyses, the results remained robust, with about 100,000-200,000 deaths that could be prevented yearly by 2050 in France. InterpretationThe present study highlights the considerable potential health benefits that trajectories toward net-zero emissions can bring, especially through shifts toward sustainable diets. These results reinforce the strong convergence of environmental and human health issues in the agri-food sector. FundingFrench High Council for the Future of Health Insurance (HCAAM) and the National Agency for Ecological Transition (Ademe). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSFood systems are a significant contributor to climate change and in parallel, dietary risks are one of the leading causes of all-cause mortality globally, notably in high-income countries such as France. A recent systematic review by Moutet et al. revealed that only two studies evaluating health co-benefits through dietary shifts in net-zero GHG emissions scenarios were published to date. This suggests a convergence and a possible win-win situation between climate change and human health challenges regarding food production and consumption. In order to face the climate crises, governments around the world, and particularly those of the countries historically the largest contributors to climate change, must cut their greenhouse gas emissions to achieve net-zero emission by 2050. Dietary shifts would be a major driver to pursue this objective and could bring important health benefits to the population conducting these changes. For instance, Hamilton et al. showed that dietary changes in line with the Paris Agreements could result in 188 deaths prevented per 100,000 persons in 2040 in Germany and 141 in the UK. Added value of this studyOf the two previously published studies, only one assumed a gradual implementation of changes in diets, combined with a time lag in health effects. We also made these assumptions and considered the gradual change in consumption of thirteen food groups for which recent meta-analyses provided all-cause mortality dose-response relationships with a high level of quality. This study is also among the first to combine nutritional and environmental optimization, through four scenarios; all of which are expected to lead to net-zero emission by 2050 via very contrasting climate change mitigation trajectories. Nevertheless, all of them require a dietary shift toward more plant-based foods. We conducted a health impact assessment for France and showed that achieving net-zero emission by 2050 while considering nutrition references set by national guidelines would provide health co-benefits. Depending on the scenarios, health gains could range from 19% to 24 % of all-cause mortality prevented in the adult French population in 2050, compared to a scenario assuming that we would maintain the current observed dietary habits in the future. Implications of all the available evidenceThis study adds to the available evidence that taking action to mitigate climate change is an opportunity to strongly improve public health. Engaging populations in a shift toward a healthier and more sustainable diet could bring major human health and environmental benefits.