Environmental Science & Technology

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.

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.

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.

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.

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.

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.

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.

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

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.

Ambient air pollution has been associated with increased incidence of chronic disease and is estimated to contribute towards 4.2 million early deaths annually. Whilst the health impacts are well described, less is understood about the underlying biological mechanisms, particularly when considering the co-occurrence of multiple pollutants. Using an atmospheric chemistry transportation model (EMEP4UK), we generate pre-baseline sampling pollution exposure estimates for eight pollutants in Generation Scotland (N = 22,071, recruited between 2006 - 2011). Cox-proportional hazard models reveal associations between pollution exposure and all-cause dementia (PM2.5) and myocardial infarction (NO3_Coarse) over 18 years of follow-up. We perform Bayesian multivariate epigenome-wide (N = 18,512, Illumina EPIC v.1) and proteomic (N = 15,314, 133 mass-spectrometry proteins) association studies, revealing 11 pollutant-methylation associations and 140 pollutant-protein associations. We identify positive associations between exposure (PM2.5 and NO3_Fine) and epigenetic age-acceleration (PhenoAge epigenetic clock). Furthermore, we explore the development of pollutant EpiScores, assessing these in holdout and independent test sets. Our results enhance knowledge of molecular correlates of air pollution exposure, whilst providing further evidence of contributions of air pollutants to chronic disease.

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.

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.

BackgroundGlyphosate-based herbicides are among the most widely used agricultural chemicals globally. Concerns regarding their carcinogenic potential, particularly in relation to non-Hodgkins lymphoma (NHL), persist despite multiple prior systematic reviews and meta-analyses. However, these reviews have demonstrated important methodological limitations and inconsistent analytic decisions, limiting confidence in their conclusions. ObjectiveTo conduct a rigorous, up-to-date systematic review and meta-analysis of observational studies examining the association between glyphosate-based herbicide exposure and risk of NHL and its subtypes, while addressing methodological and analytic shortcomings of prior syntheses. MethodsWe searched MEDLINE (1970-February 26, 2026) and EMBASE (inception-February 26, 2026), supplemented by reference list review. Eligible studies included cohort, case-control, and pooled analyses reporting effect estimates (or sufficient data) for glyphosate exposure and NHL incidence. Two reviewers independently assessed risk of bias using the Newcastle-Ottawa Scale (for primary studies) and structured criteria for pooled analyses. Random- and fixed-effects meta-analyses were conducted using inverse-variance methods. Heterogeneity was evaluated using Cochrans Q and I{superscript 2} statistics. Publication bias was assessed using standard and contour-enhanced funnel plots. Sensitivity analyses addressed overlapping cohorts, hazard ratio inclusion, exposure definitions, and model overfitting (events-per-variable considerations). Certainty of evidence was graded using GRADE. ResultsSeventeen publications were identified, representing 20 unique study populations; after accounting for overlap, 10 primary datasets were included in quantitative synthesis. Five studies were assessed as low risk of bias, four as moderate risk, and one as high risk. For ever exposure, the random-effects model across all eligible datasets yielded an odds ratio (OR) of 1.11 (95% CI: 0.98-1.27), with moderate heterogeneity (I{superscript 2}{approx}53%). In sensitivity analyses excluding hazard ratio-only studies and overlapping cohorts, pooled ORs ranged from 1.19 to 1.23, with estimates approaching or reaching statistical significance depending on modeling assumptions. For the highest exposure categories, the random-effects model demonstrated a statistically significant association (OR{approx}1.38; 95% CI: 1.00-1.90), with moderate heterogeneity (I{superscript 2}{approx}61%). Sensitivity analyses excluding selected pooled cohort estimates strengthened the association (OR{approx}1.47; 95% CI: 1.04-2.06). Analyses incorporating alternative cumulative exposure metrics yielded similar significant associations (OR{approx}1.33-1.45) with low or absent residual heterogeneity. Subtype analyses suggested elevated risks particularly for diffuse large B-cell lymphoma and follicular lymphoma in certain datasets. Publication bias assessments revealed evidence of small-study effects in some models, though contour-enhanced analyses suggested that not all asymmetry was attributable to selective publication. Overall certainty of evidence was graded as moderate for highest exposure analyses and low-to-moderate for ever-exposure analyses due to residual heterogeneity and observational design limitations. ConclusionsThis updated synthesis indicates that while associations with ever exposure to glyphosate are modest and sensitive to analytic decisions, higher levels of exposure are consistently associated with increased odds of NHL. Findings are robust across multiple sensitivity analyses addressing overlapping data, exposure classification, and model overfitting. These results support a dose-related association between glyphosate-based herbicide exposure and NHL risk and underscore the need for continued surveillance, improved exposure characterization, and prospective cohort analyses with minimized loss to follow-up and transparent analytic reporting.

BackgroundNontyphoidal Salmonella enterica (NTS) is a major public-health threat in the United States of America (U.S.). Evaluating associations between serovars, exposure sources, and settings in multistate outbreaks can reveal the drivers of NTS transmission and guide prioritization of targeted prevention and control strategies. MethodsWe analyzed multistate animal-contact NTS outbreaks reported to the CDC National Outbreak Reporting System during 2009-2022. We calculated incidence rates per 10 million population-years (MPY) and assessed temporal trends using Joinpoint regression. We constructed interstate co-occurrence networks linking serovars, exposure sources, settings, and states, and applied a random forest classifier to identify variables most useful for distinguishing outbreak profiles. ResultsWe identified 177 multistate outbreaks (0.06 per 10 MPY) involving 40 serovars. Incidence significantly declined from 2009 to 2013 and remained stable thereafter. Random forest rankings identified birds and reptiles as the most influential exposure sources and agricultural feed stores and residential homes as the most influential exposure settings in distinguishing outbreak profiles. Co-occurrence network analysis revealed two major communities. The first included outbreaks involving serovars Enteritidis and Infantis, bird exposure source, and agricultural feed stores or farms as exposure settings, with hubs across the Midwest, Northeast, and Southern regions. The second community involved outbreaks linked with reptiles and mammals as exposure sources, residential homes and farms as exposure settings, and serovars Hadar, Typhimurium, and Braenderup, which were concentrated in the Western and Southern regions. ConclusionsMultistate animal-contact NTS outbreaks clustered into distinct serovar-exposure, source, setting, and region patterns, suggesting different NTS outbreak transmission pathways. The persistence of NTS serovars across states, diverse animal-contact sources, and exposure settings underscores the ongoing zoonotic transmission risk at the human-animal and environmental interfaces. A region-specific One Health approach to prevent and control NTS outbreaks is suggested to reduce the health burden.

BackgroundCurrent syndromic guidelines for diarrhea treatment miss watery Shigella cases, leading to undertreatment of children who may benefit. Incorporating fecal inflammatory biomarkers into diagnosis may improve case identification. MethodsWe conducted an ancillary analysis using samples from six sites (The Gambia, Kenya, Malawi, Bangladesh, Pakistan, and Peru) from the Enterics for Global Health (EFGH)-Shigella surveillance study, a facility-based hybrid study of children aged 6-35 months with diarrhea. Four fecal biomarkers were quantified by enzyme-linked immunosorbent assays at enrollment: myeloperoxidase, calprotectin, neutrophil gelatinase-associated lipocalin (lipocalin-2), and hemoglobin. An ensemble model with leave-one-site-out cross-validation was used to predict watery shigellosis, incorporating biomarkers and nine clinical and socio-economic predictors. We compared the predictive performance of the algorithm using: a) all predictors (including biomarkers); b) all non-biomarker predictors; c) all predictors (with selected biomarkers). ResultsBetween June 2022 and August 2024, a total of 4,191/9,476 (44.2%) children presented with watery diarrhea and had their whole stool tested for the biomarkers and 4,083 stool samples or rectal swabs were tested by qPCR; 735 (18.0%) had Shigella-attributable diarrhea by qPCR. The full model incorporating all 13 predictors achieved an area under the curve (AUC) of 0.75 [95% CI: 0.67-0.78], with a sensitivity of 0.67 and specificity of 0.75. Excluding biomarkers reduced model performance by 8% (AUC 0.67, 95% CI: 0.61-0.70). Adding hemoglobin alone improved the models discriminatory ability by 7%, while further adding myeloperoxidase had marginal contribution (1%), and lipocalin-2 (0%) and calprotectin none (0%). ConclusionFecal hemoglobin substantially improved prediction scores for watery shigellosis. Consequently, implementation of point-of-care assays for hemoglobin could improve clinical diagnosis in these settings and inform appropriate antibiotic treatment. Author SummaryDiarrhea remains a leading cause of illness in young children, with the bacteria Shigella being a major contributor. Current treatment guidelines recommend antibiotics only when diarrhea is visibly bloody. However, many children with Shigella infection have non-bloody, watery diarrhea and are therefore not treated, despite needing care. In this study, we explored whether simple markers of gut inflammation found in stool could improve the identification of Shigella infection. We analyzed stool samples from over 4,000 children across six countries in Africa, Asia, and South America and combined these markers with basic clinical information. We found that detecting small, invisible amounts of blood in stool (fecal hemoglobin) improved identification of non-bloody Shigella diarrhea by 7%. Other inflammation markers added little benefit. By combining fecal hemoglobin with childs age and stool frequency, we developed a simple score that matched performance of the full model enhancing its practical use. This approach outperformed current guidelines, which only treat bloody diarrhea, and was more accurate than treating most children without testing. Importantly, fecal hemoglobin tests are inexpensive and commercially available. Using them at the point of care could help health workers better target antibiotics, improve child outcomes, and promote responsible antibiotic use in low-resource settings.

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.

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.

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data with wastewater data. Furthermore, only a subset of existing approaches have been able to handle missing data without imputation and to handle datasets with different sampling times or intervals. Here, we develop a statistically rigorous, yet lightweight, approach to infer and forecast time-varying effective reproduction numbers (Rt values) using longitudinal wastewater virus concentrations either alone or jointly with additional data streams including case incidence data and serological data. Our approach relies on a state-space modeling approach for inference and forecasting, within the context of a simple bootstrap particle filter. We first describe the structure of our underlying disease transmission process model as well as our observation models. Using a mock dataset, we then show that Rt can be accurately estimated by interfacing this model with case incidence data, wastewater data, or a combination of these two data streams using the bootstrap particle filter. Of note, we show that these data streams alone do not allow for reconstruction of underlying infection dynamics due to structural parameter unidentifiability. We then apply our particle filter to a previously analyzed SARS-CoV-2 dataset from Zurich that includes case data and wastewater data. Our analyses of these real-world datasets indicate that incorporation of process noise (in the form of environmental stochasticity) into the state space model greatly improves our ability to reconstruct the latent variables of the model. We further show that underlying infection dynamics can be made identifiable through the incorporation of serological data and that the bootstrap particle filter can be used to make forecasts of Rt, case incidence, and wastewater virus concentrations. We hope that the inference approach presented here will lead to greater reliance on wastewater data for disease surveillance and forecasting that will aid public health practitioners in responding to infectious disease threats.

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.

Accurate identification of unknown pathogens is critical for medicine and public health, yet current metagenomic workflows remain heavily dependent on specialized bioinformatics expertise and manual interpretation, creating substantial bottlenecks in time-sensitive diagnostic settings1. The key challenges lie in achieving precise species identification amidst high background noise and translating complex microbial data into clinically actionable insights2,3. Here we present the Global Pathogen Analysis System (GPAS), an integrated computational framework that combines rapid and accurate pathogen identification with large language model (LLM)-based semantic interpretation. Central to GPAS is a dynamic-library alignment mechanism informed by prior probabilities of inter-species misclassification. By integrating a hybrid machine learning model that couples elastic neural networks with Bayesian inference, this approach substantially reduces both false positives and false negatives, achieving species-level accuracy superior to existing state-of-the-art tools. To enable clinical interpretation, we constructed a unified microbial knowledge graph integrating global metagenomic and metaviromic sample repositories, and trained a pathogen-specialized LLM agent. Through end-to-end reinforcement learning, the agent autonomously executes multi-step reasoning workflows extracting pathogen-specific insights from complex data and generating human-readable, evidence-based reports. Application to throat swab samples demonstrates that GPAS not only accurately identifies pathogenic microorganisms but also reveals how SLE-associated immune dysregulation reshapes the respiratory microbiome and promotes pathobiont overgrowth, providing clinically instructive interpretations. By substantially lowering technical barriers to pathogen identification, GPAS offers an accessible yet powerful platform for clinical diagnostics, public health surveillance, and microbiome research. The system is freely available at: https://gpas.nh.ac.cn/.