One Health

Background: Listeria monocytogenes causes invasive infections with high mortality. However, ecological studies linking human incidence to livestock and weather remain scarce. This retrospective study analyzed the geographical distribution of human listeriosis in Spain using nationwide hospitalization data, livestock censuses, and meteorological records. Methods: Negative binomial regression models, spatial autocorrelation (Moran's Index), and bivariate local indicators of spatial association (LISA) analysis were applied. Results: Between 2016 and 2024, 4,391 hospital admissions were recorded. The national mean incidence was 1.03 per 100,000 inhabitants, peaking in 2019. Bovine density was the primary environmental driver, with an 11% increased risk per 10 heads/km2. Weather-livestock synergies were observed: bovine-associated risk increased exponentially at higher temperatures ([~]21 C) and lower humidity (53%), whereas caprine-associated incidence peaked at cooler temperatures ([~]13 C). Spatial mapping revealed northern clusters for bovine reservoirs and southern segregation for caprine reservoirs, with no associations for ovine or porcine livestock. Conclusions: Zoonotic risk is environmentally conditioned, with weather acting as a biological trigger. This study highlights the critical role of ruminant farming practices and the need for a "One Health" surveillance approach that integrates meteorological and farming data to predict seasonal outbreaks and optimize food chain safety.

Antimicrobial resistance (AMR) is a critical One Health challenge at the interface of agriculture, the environment and human health. In Ireland, cattle production systems generate large volumes of manure and slurry, which are routinely applied to land. However, data on the prevalence and environmental persistence of clinically important resistance phenotypes remain limited. This study investigated the occurrence, characterisation and persistence of extended-spectrum {beta}-lactamase (ESBL) and carbapenemase-producing Enterobacterales (CPE), as well as fluoroquinolone-resistant bacteria on Irish cattle farms. A total of 450 pooled bovine faecal samples were collected from farms across 25 counties, screened using selective culture, antimicrobial susceptibility testing, and MALDI-TOF MS identification. Carbapenemase activity in presumptive CPE isolates was confirmed using phenotypic and biochemical assays, including MALDI-TOF MBT STAR analysis to demonstrate carbapenem hydrolysis. In parallel, a controlled persistence study evaluated the survival of phenotypically resistant ESBL and CPE bacteria in bovine slurry under simulated seasonal conditions. Overall, 29.6% of farms were positive for ESBL and/or CPE, with ESBL only resistance most prevalent (13.7%), followed by combined ESBL and CPE phenotypes (7.5%). Fluoroquinolone resistance was frequently detected and commonly co-occurred with {beta}-lactam resistance, indicating multidrug resistance. Escherichia coli was the dominant carrier of both ESBL and carbapenemase phenotypes. Persistence experiments demonstrated sustained phenotypic resistance in slurry for up to nine months under simulated winter conditions and up to three months during simulated summer conditions, with resistance becoming undetectable following extended verification. These findings demonstrate that clinically important AMR phenotypes are embedded within Irish cattle production systems and can persist in agricultural waste streams, highlighting slurry as a significant environmental reservoir with implications for environmental dissemination and One Health surveillance.

The 2026 Andes virus (ANDV) outbreak aboard the MV Hondius cruise ship resulted in 11 confirmed cases and 3 deaths and raised questions regarding the origin of infection, with much media focus resting on a landfill in Tierra del Fuego. We evaluated competing exposure hypotheses by assembling open data on reservoir host occurrences, rodent serosurveys, and historical ANDV outbreaks along with the index case's self-documented, publicly-posted citizen-science digital record of their movements. Historical outbreaks associated with human-to-human transmission were concentrated within a restricted region spanning southern Chile and adjacent western Argentina, coinciding with the distribution of the principal reservoir host, Oligoryzomys longicaudatus, and areas identified as elevated risk by multiple ecological and genetic models. The index case was documented within this transmission focus shortly before the putative exposure period. Together, our open data approach was able to rapidly locate initial exposure within the established ANDV transmission focus and develop scenarios to prioritise further epidemiological investigation.

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

Recent zoonotic coronavirus outbreaks have sparked an interest in understanding coronaviruses circulating in animal reservoirs. While bats are recognised as major reservoirs for these viruses, coronaviruses have also been detected in a wide range of terrestrial small mammals, particularly rodents. However, coronavirus diversity in these mammals, specifically in Europe, is still poorly characterised. In this study, we detected and characterised coronaviruses in terrestrial small mammals and bats from Belgium, France, Germany, and Ireland. We screened tissue samples from rodents and shrews and environmental bat guano using pan-coronavirus real-time and conventional PCRs, with positive results confirmed by sanger sequencing. Among 2667 bat and 1152 terrestrial mammal samples, we detected 59 positive samples, 36 from bats and 23 from rodents, from which we recovered 60 coronavirus sequences, spanning both Alphacoronavirus and Betacoronavirus lineages. Through next generation sequencing we identified one Pipistrellus pygmaeus bat guano sample carrying two distinct alphacoronaviruses, representing Nyctacovirus and Pedacovirus subgenera. Among rodents, most coronavirus-positive samples originated from a zoo in Belgium, where several CoV lineages co-circulated. The identified rodent coronaviruses clustered among previously described rodent-associated lineages, some of which also include human and other mammal-derived coronavirus sequences. Consistent with previous studies, we found sarbecoviruses in horseshoe bats (particularly Rhinolophus ferrumequinum and R. hipposideros) and merbecoviruses in Plecotus auritus bats, and these viruses are distantly related to the human coronaviruses of the respective families. Similar to bat coronaviruses, some rodent coronaviruses strains seemed to be association with specific rodent species. In conclusion, our study enhances the understanding of coronaviruses in rodents and bats, revealing potential hotspots for these viruses and their implication for human and animal health. These findings underscore the necessity for continued surveillance of coronaviruses in wildlife reservoirs to mitigate future zoonotic spillover risks.

Land use change is widely recognized as a major driver of emerging zoonotic diseases. Yet, while forest plantations are expanding worldwide, their consequences on the circulation of infectious agents in wildlife remain poorly understood. We investigated how native pine plantations and broadleaved habitats shape rodent-associated infectious hazard in the generalist wood mouse (Apodemus sylvaticus), in southwestern France. Using a multi-season survey, we screened 424 wood mice for a broad range of infectious taxa and compared infection probability and infected-host abundance among pine plantation edges, broadleaved hedgerows embedded within pine plantations and broadleaved forests. Using a combination of linear mixed model and joint species distribution modelling, we tested whether infectious hazard differed between pine plantations and broadleaved forests, whether broadleaved hedgerows shifted these patterns towards those observed in broadleaved forests, and how these habitat-related patterns varied across seasons. We detected 22 potentially infectious taxa, including nine recognized zoonotic bacteria. Season strongly influenced infectious patterns, while habitat effects were taxa and season dependent. In spring, pine plantation edges increased infection probability for Bartonella grahamii and Neoerhlichia mikurensis compared to broadleaved forest, but their strong negative effect on wood mouse abundance resulted in consistently lower infected-host abundance for most infectious taxa. Broadleaved hedgerows showed distinct and season-dependent patterns with infectious profiles resembling those of pine edges in spring, but with higher infectious hazard than both other habitats for several taxa in autumn. Our results highlight that habitat effects on infectious hazard cannot be inferred from infection probability alone. Pine plantations may reduce rodent-associated zoonotic hazard through their strong negative effect on host abundance, despite increasing infection probability for some agents. Forest diversification and restoration initiatives, such as broadleaved hedgerows here, may have unforeseen epidemiological consequences, underscoring the need to integrate disease ecology into forest management and restoration to anticipate variation in zoonotic risks, while maximizing ecological benefits.

Background: Bovine respiratory disease (BRD) is a major cause of antimicrobial use in cattle, raising concerns about antimicrobial resistance (AMR). This systematic review estimated pooled AMR proportions in BRD pathogens: Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni, and assessed temporal and geographic trends in resistance. Methods: Five antimicrobials commonly used for BRD treatment were evaluated: penicillin G (PEN), tetracycline (TET), florfenicol (FLO), tulathromycin (TUL), and enrofloxacin (ENR). Following PRISMA guidelines, 1,097 records were screened and 24 studies met inclusion criteria. Study quality and risk of bias were assessed, and random-effects meta-analyses estimated pooled proportions of resistant isolates by antimicrobial, continent and period. Results: A total of 13,949 bacterial isolates were analysed including P. multocida (5,615), M. haemolytica (5,585), and H. somni (2,749). TET resistance was highest across all pathogens (12-30%), followed by TUL (6-11%), PEN (4-12%), FLO (0.5-1.6%) and ENR (0.2-1.1%). TET resistance in M. haemolytica and P. multocida, and FLO resistance in P. multocida were significantly more frequent in North America than in Europe (p[≤]0.05). TUL resistance increased significantly in P. multocida after 2013 (p=0.038), with similar non significant trends in M. haemolytica and H. somni. FLO resistance decreased significantly in H. somni after 2013 (p=0.013). Egger's test identified significant funnel plot asymmetry for ENR resistance in H. somni and PEN resistance in M. haemolytica, suggesting possible small study effects. Conclusion: These results offer insights into distribution of phenotypic resistance across pathogens and regions, informing BRD antimicrobial treatment guidelines and One-Health antimicrobial stewardship aimed at preserving the effectiveness of medically important antimicrobials.

Domestic dogs may influence human microbiota and act as reservoirs or sentinels for zoonotic bacteria, yet little is known about these dynamics in Southeast Asia. Using full-length 16S rRNA gene sequencing from rectal swabs, we analyzed microbiota from dog-owner pairs from four villages of rural Thailand (Nan province) to assess microbial diversity, shared taxa, and zoonotic potential. Dogs exhibited higher microbial and putative pathogen diversity than humans, particularly during the rainy season. While dog-owner pairs shared more bacteria than unrelated individuals, overall overlap remained limited - likely driven by their dogs free-roaming lifestyles and broader environmental exposures. However, co-occurrence of putative zoonotic pathogens such as Shigella spp. and Klebsiella pneumoniae suggests dogs could serve as useful indicators for specific public health risks, with further targeted pathogen screening needed to validate these results. Our findings highlight the value of integrating dogs into zoonotic surveillance programs, particularly in regions with free-ranging dog populations and close human-animal contact.

The gut-airway axis is increasingly recognised as an important determinant of respiratory health in livestock. However, the interactions among respiratory infections, microbial communities, mucosal barrier function, and microbial metabolism that underpin this axis remain poorly understood. Here, we combined longitudinal immunological, mucosal, microbiological, and metabolic analyses to characterise host-microbiota interactions during antibiotic treatment and experimental bovine respiratory disease (BRD). We found that the bovine respiratory tract is organised as a hierarchically structured mucosal ecosystem, with compartment-specific mucin expression patterns associated with distinct microbial communities. Within this framework, increasing respiratory pathogen loads were associated with greater clinical severity, oxidative stress, acute inflammation, and targeted alterations in mucosal regulation and microbial composition, while serum amyloid A emerged as the host biomarker most closely reflecting infection intensity. Although infection altered host and microbial states, antimicrobial treatment represented the dominant ecological perturbation. Antibiotic exposure reshaped both respiratory and intestinal microbial communities, reducing diversity, increasing community drift, and driving coordinated ecological transitions across the gut-airway axis that exceeded the effects of infection alone. Importantly, these compositional changes were accompanied by profound reductions in acetate, propionate, butyrate, and valerate, identifying short-chain fatty acids as a major functional consequence of gut microbiota disruption. Integrative multi-omics analyses revealed that mucosal organisation, microbial diversity, microbial metabolism, and immune activity were embedded within a highly structured gut-airway network. Together, these findings support a holobiont view of BRD in which disease outcomes emerge not only from pathogen-host interactions but also from ecological disturbances that propagate across interconnected mucosal compartments. Notably, antimicrobial treatment affected ecosystem structure and function more than infection itself, highlighting potential trade-offs between pathogen control and microbiota stability. Preserving microbial resilience and its metabolic functions may therefore represent an important component of future strategies to improve respiratory health and disease resilience in cattle.

Antimicrobial resistance is a global One Health threat connecting antimicrobial use in human health, animal husbandry, and companion animal health with concern for persistence in environmental reservoirs. Complicating this threat is the gap that exists in our understanding of the intersection of antimicrobial resistance among humans and our companion and food production animals. This study applies a visual and statistical approach to further understand antimicrobial resistance characteristics and relatedness of Escherichia coli found in humans, dogs and cattle. The primary study collection included third-generation cephem resistant (3GCR) E. coli isolates recovered from human, dog, and cattle gastrointestinal specimens and secondarily human and dog isolates recovered from infection sources, primarily urinary tract infections. We previously reported on the transferable gene targets encoding for 3GCR in colonizing isolates and here further identified the familial-like clonal group of each resistant isolate using a novel clonotype diagnostic (septatyping). Together, this approach identified distinct genetic signatures among these 3GCR isolates, many of which are peculiar to their respective host species. We found that the overlap measured by the correlation in abundance of shared clonotypes of resistant E. coli among hosts revealed statistically greater similarity between dogs and humans than between cattle and humans. Yet, the reservoir of AMR genes was highest in cattle and secondarily dogs. While these findings demonstrate that clonotypes of 3GCR E. coli in humans, dogs and cattle display significant host specificity, transient colonization from a host enriched with resistant strains may still pose considerable risk of horizontal transfer of resistance genes between hosts living in proximity to each other.

Background: Previous studies indicated that regional fluctuations in COVID-19 impact were associated with socioeconomic, demographic and environmental factors early in the pandemic. It is unclear whether factors associated with regional COVID-19 mortality and SARS-CoV-2 infections changed over the course of the pandemic. Methods: We evaluated the association of ecological factors with municipality-specific COVID-19 mortality in the national population of Austria between January 2020 and June 2023. Analyses were conducted separately per calendar year and pooled across years via random-effects meta-analysis. Outcomes were analysed using univariable and multivariable zero-inflated negative binomial (ZINB) regression, including annually averaged environmental exposome exposure (including air pollution and UV radiation), socioeconomic, demographic and infection dissemination predictors with significance threshold set to p<0.005. Secondary analysis modelled SARS-CoV-2 infections using of negative-binomial model (NB). Two additional approaches for smaller predictor set selection were applied as sensitivity analyses. Results: Within the entire observation period 18,430 COVID-19 deaths were reported. In univariable analyses (pooled RR (99.5% CI)), COVID-19 mortality was significantly associated with the proportion of commuters (0.79 (0.73-0.86)), residents aged 75-84 (1.18 (1.01-1.37)) and [&ge;]85 years (1.29 (1.04-1.60)), residents with at most compulsory education (1.15 (1.02-1.29)), employed residents aged 15-64 (0.89 (0.85-0.93)), and homeowners (0.91 (0.83-0.99)), as well as with precipitation (0.94 (0.89-0.99)) and the average time with a mean temperature of 5-15 C (0.95 (0.90-0.99)). In multivariable analyses the share of population aged 85 or older (1.30 (1.09-1.54)) and home ownership (0.92 (0.87-0.98)) were independently significant. Mortality predictor estimates were relatively stable across pandemic years. Across 5,963,882 documented SARS-CoV-2 infections share of employed in 15- to 64-year-olds (1.01 (1.01-1.02)), part-time employment rate (1.01 (1.00-1.02)) and district level nursing care allowance recipiency (0.97 (0.95-0.98)) were associated with detected infections. Overnight stays, population 85 or older, income, compulsory education or less and air pollution all had multiple significant estimates with opposite directionality, leading to non-significant overall associations. Sensitivity analyses outcomes mirrored main analyses outcomes. Conclusions: Throughout the pandemic in Austria, demographic and socioeconomic factors were associated with COVID-19 mortality and infections, but many associations, especially for infections, were unstable over time. These findings underscore the importance of structural vulnerabilities in epidemic impact.

Background: Lean Healthcare has been adopted as an approach to reducing waste, enhancing process efficiency, and optimizing clinical and administrative workflows. However, empirical evaluations aimed at establishing a causal relationship between Lean Healthcare interventions and outcomes remain limited. This evidence gap is particularly pronounced in the context of large-scale implementations in low- and middle-income countries, where more efficient allocation could yield substantial improvements in population health. Methods: We conducted an impact evaluation study employing a staggered difference-in-differences approach to estimate the effects of the Lean Healthcare in Emergency Departments (ED) initiative in the Brazilian context. The intervention was implemented between January 2017 and December 2025 across 309 hospitals. We analyzed hospital-level indicators of the number of hospitalizations and the average length of stay (in days) among patients admitted through urgent or emergency pathways. Results: Our estimates suggest that the intervention was associated with an average increase of 38.6 admissions per hospital per month (p = 0.033), corresponding to approximately a 5.9% increase relative to the monthly admission volume. We also observed a reduction of 0.184 days in the average length of stay (p = 0.058), equivalent to an approximately 3.0% decrease in hospitalization time. These effects exhibited distinct temporal dynamics: the effect on the number of hospitalizations emerged gradually and increased with the duration of the intervention, whereas the reduction in length of stay became evident earlier. Conclusion: This study provides evidence of a causal effect of Lean Healthcare implementation in emergency departments on operational performance. The simultaneous increase in admissions and reduction in length of stay suggests improved bed productivity and effective capacity without expanding physical infrastructure. These findings support the use of flow-management methodologies as an effective strategy for mitigating emergency department overcrowding while increasing service capacity.

Background: Multiple factors can drive heat susceptibility, but are typically analyzed individually. This study investigates high-dimensional heterogeneity across multiple domains in the effect of hot days on emergency hospitalizations across German districts. Methods: We obtained emergency hospitalization rates per 1,000 in 3-day windows for the summers of 2005-2023 from the German Diagnosis-Related Groups-Statistic, linked with spatio-temporal temperature information. A hot day was defined as at least one day above the district-level 98th percentile heat index. Multiple characteristics of the demographic, health/social, and environmental domains were selected as potential effect modifiers. We implemented a generalized random forest, where outcome and propensity scores were modeled with fixed effects estimators to control for seasonality and district-level confounding. We divided the conditional average treatment effects (CATE) into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: We found substantial variation across districts regarding heat-related emergency hospitalizations. The top three indicators for predicting this effect heterogeneity were the percentage of people seeking protection, population below age 6, and air pollution levels. The observed indicator profile for the most susceptible districts was characterized by a larger percentage of vulnerable populations, higher car density, and fewer hospital beds. Conclusion: There is substantial variation in heat susceptibility across and within districts in Germany. We propose an approach that can help understanding such complex interplay of indicators from different domains driving heterogeneity.

Mycobacterium bovis causes tuberculosis (TB) in cattle, badgers (Meles meles), other wildlife, and humans, posing major animal and public health challenges in Britain and Ireland. Shared genotypes between cattle and badgers have been reported at regional scales, but whether this concordance holds at finer spatial scales, where control interventions operate, and whether it varies by genotype combination, remains unknown. Resolving this is critical for interpreting cross-species transmission risk and designing targeted control measures. We analyzed 1,248 M. bovis isolates from cattle and badgers (Northern Ireland, 1986-2018), including samples from a small, densely sampled intervention research area, using Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA) and kernel discriminant analysis (KDA) across multiple spatial scales (25-95% density contours). We identified 36 MLVA types with four shared between species and one (MLV06) endemic to the area. Spatial concordance varied by type combination and scale: MLVA004-MLVA297 showed strong, bidirectional, scale-robust concordance; MLVA006-MLVA297 was concordant only at fine spatial scales; MLVA004-MLVA006 showed weak, asymmetric concordance; and no concordance was detected when all three types were analysed together. Our findings reveal spatial concordance between shared M. bovis genotypes varied by genotype combination and scale, indicating genotype-specific dynamics within this multi-host system.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [&ge;]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

The contribution of environmental reservoirs to antimicrobial resistance (AMR) acquisition in humans remains poorly quantified, despite extensive evidence that resistant bacteria and resistance genes circulate through wastewater, surface waters, food and other environmental matrices. The central difficulty is no longer to demonstrate that these reservoirs are connected, but to quantify the direction and relative contribution of the pathways linking environmental AMR to human acquisition. Here, we synthesize the main processes that need to be represented at the interface between the environment and humans and propose a mechanistic framework for Escherichia coli producing extended spectrum beta lactamases (ESBL Ec), linking human, water and food compartments. The framework explicitly represents contamination of the environment by humans and multiple routes of human exposure to environmental reservoirs, including drinking water, recreational water exposure and food contamination mediated by irrigation. We apply the model to two contrasting illustrative settings, France and Madagascar, and use scenarios involving antibiotic use, environmental contamination and climate to explore how changes in specific drivers propagate across compartments. The simulations show responses that depend strongly on the compartment, while sensitivity analyses identify wastewater treatment efficiency and parameters describing transmission, exposure and environmental selection at specific interfaces as influential components of the system. The framework is used to explore how different transmission pathways and environmental drivers can shape AMR dynamics across interconnected human and environmental compartments. By comparing contrasting scenarios and identifying the parameters that most strongly influence model behaviour, we highlight both potential leverage points and critical gaps in current empirical knowledge. More broadly, this work illustrates how decomposing One Health AMR into tractable reservoir interfaces can help structure future data collection and progressively improve our ability to quantify transmission between reservoirs.

Population-scale pathogen surveillance is limited worldwide, particularly in resource-constrained settings, where clinical systems only monitor select priority pathogens and reach only those who can access healthcare. Wastewater-based epidemiology addresses many of these gaps, offering cost-effective surveillance that is adaptable across diverse implementation contexts. In Lagos, Nigeria, a high enteric pathogen burden coincides with a complex sanitation landscape. However, circulating pathogen diversity in Lagos is largely uncharacterised outside of outbreak contexts. Here we characterize human-associated pathogen diversity in Lagos with virus-enriched metagenomic and metatranscriptomic sequencing of untreated wastewater sampled from open drainage canals from July-August 2024. We detected a diverse human-associated virome dominated by enteric adenoviruses, astroviruses and caliciviruses. We recovered numerous partial and near-complete genomes from pathogens of public health concern, including noroviruses, enteroviruses and sapoviruses. Although our enrichment targeted viruses, we detected diverse bacterial pathogens, including recurrent detection of Vibrio cholerae, other enteric bacteria, and zoonotic pathogens such as Streptococcus suis. We also detected antimicrobial resistance genes, including aminoglycoside, beta-lactam and fluoroquinolone resistance and clinically important genes such as mcr. Together, these results establish a regional baseline of pathogen diversity and demonstrate the utility of virus-enriched sequencing for integrated wastewater surveillance.

Objectives: Heatwaves drive surges in acute care demand, yet heat-health action plans (HHAPs) lack tools to scale emergency care responses. This study validated a forecasting tool for heatwave-related emergency department (ED) demand across four clusters comprising the emergency care network of Ticino Canton, Switzerland. Methods: This retrospective, ecological, time-series study modeled daily mean temperature and ED presentations using a distributed lag non-linear model framework with autoregressive and calendar adjustment components. The model was trained on summers (June-August) 2016-2024 and tested on summer 2025, defining heatwaves as daily mean temperature >25 {degrees}C for [&ge;]3 consecutive days. Results: A non-linear temperature-demand association was shown across the clusters, with ED visits increasing at higher temperatures. Model fit varied across clusters (R2 0.389-0.672), and the reference model consistently outperformed naive baselines. During two heatwaves in 2025, prediction errors remained within acceptable ranges. Baseline demand, heatwave occurrence, and model performance varied substantially within the canton. Conclusions: These findings have implications for the design of HHAPs, suggesting that emergency care preparedness should rely on locally calibrated demand forecast rather than standardized activation protocols.

The growth and performance of cattle is dependent on efficient feed conversion, facilitated by the gut microbiome, including both prokaryotic and eukaryotic organisms. Extrinsic factors such as management practices and intrinsic factors like age and genetics can shape these communities. To characterize the relative effects of such factors on the prokaryotic and eukaryotic microbiomes, fresh fecal samples from dairy heifers (n = 8), beef heifers (n = 8), bred beef heifers (n = 8), and adult beef cows (n = 8) were subjected to paired 16S rRNA (V4) and a novel 18S rRNA (V4-V5) metabarcoding assay, and 18S rRNA amplicons were annotated using two databases in parallel. Taxonomic resolution of 18S rRNA amplicons was excellent, with SILVA and EUKARYOME databases each providing better resolution of different groups of organisms. While no differences in eukaryotic richness or alpha diversity were detected, beta diversity differed in all pairwise comparisons except between beef heifers and bred beef heifers. Prokaryotic richness was lower in adult beef cows than the other groups, and beta diversity differed in all pairwise comparisons. Adult beef cows were the most distinct, characterized by replacement of fungal organisms with protozoa and loss of thermophilic bacteria in the eukaryotic and prokaryotic compartments, respectively. Among numerous differentially abundant features, dairy heifers harbored greater abundance of Cooperia oncophora while beef heifers contained greater abundance of Buxtonella sulcata and Mucor. Correlation network analysis revealed multiple significant positive and negative associations between bacterial, fungal, and protozoal features during maturation of the fecal microbiome. Collectively, these data highlight the richness and diversity of both the prokaryotic and eukaryotic compartments of the bovine fecal microbiome, and the influence of management practices and age on both communities. From a technical standpoint, these data demonstrate the ability to resolve fungal, protozoal, and metazoan organisms, including potential parasites and pathogens, with a single metabarcoding assay. They also highlight the nuanced differences between databases and the value of parallel analyses using more than one database.

Urban green infrastructure is increasingly promoted as a public-health intervention, yet the biological features that determine exposure to potentially beneficial plant-derived volatile compounds remain poorly understood. Here we quantified airborne terpenes and terpenoids across five contrasting urban greenspaces in Oxford, UK, and related chemical profiles to plant diversity, vegetation structure and surrounding habitat characteristics. Ambient air samples collected across spring and summer were analysed by thermal-desorption gas chromatography-mass spectrometry, alongside field surveys of taxonomic richness, canopy cover, vegetation volume and ground composition. Terpene and terpenoid scentscapes differed strongly among sites and through time, with monoterpenes dominating detected profiles. Sites with greater plant richness and greater vegetation volume below 1.5 m consistently showed higher terpene/terpenoid abundance, whereas total vegetation volume, canopy cover and tree abundance were weaker and less consistent predictors. These relationships were specific to plant-associated chemical classes and were not observed for predominantly anthropogenic volatile compounds. These patterns were resolved across only five independent sampling sites, and the relationships reported here should therefore be considered exploratory. Our findings suggest that human exposure to plant-derived volatiles in cities may be maximised not simply by increasing tree cover, but by designing biodiverse, structurally complex lower-to-mid-storey vegetation within the breathing zone. These results provide an empirical basis for incorporating scentscape ecology into health-oriented urban greenspace design.