GeoHealth

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

In recent years, detection of local dengue cases in Florida have increased in both frequency and geographical extent. From 2022 to 2024, consecutive outbreaks in Miami-Dade County were mainly caused by a single lineage of dengue virus (DENV) serotype 3, prompting questions about changing epidemiology and a transition towards endemicity. In this study, we used mathematical modeling and genomic epidemiology to reveal the spatiotemporal dynamics and drivers of local dengue cases in Florida. We found that annual clusters and outbreaks were caused by frequent short-lived DENV introductions, primarily from the Caribbean, and did not find evidence for local trans-seasonal DENV lineage persistence. Further, we show that the climate-driven increases in local suitability for Aedes aegypti transmission and travel-associated cases were the greatest risk factors for outbreaks in Miami-Dade and the geographic expansion of dengue in Florida. Overall, while we do not yet find evidence for endemicity, we demonstrate how climatic trends are enhancing the local public health risk caused by dengue in Florida.

Ectotherm insects growth and development are dictated by temperature and humidity. Conducive habitats and the availability of resources set ideal conditions for insect population growth. Mosquitoes require water, favorable temperature, and blood meal to survive. In this research, we picked a rapidly growing megacity, Ahmedabad, in western India, to explore and establish potential linkages between disease spread and meteorological conditions. Ahmedabad, with a population of over 8 million, is experiencing changes in rain and humidity patterns, pushing the city towards changing vector-borne disease dynamics. We examined dengue cases over ten years, 2012-22, and explored their connections with two prominent climatic variables, temperature and relative humidity. Our findings indicate that stable temperature (25-27.5 {degrees}C) and humidity (> 60%) interaction is a ruling factor in spikes in dengue cases in the city. While stable temperature ranges triggers the dengue cases, RH drives the explosive phases and sustainability of such episodes. Statistically significant increasing trends in temperatures, narrowing down of the day-night temperature ranges, and increasing night temperatures provide more stable temperature regimes in a warming world thereby likely to extend the dengue season beyond the usual monsoon season. Plain Language SummaryDengue incidences have been found to be associated with mosquito population outbreaks. Every year, thousands of lives are lost due to this deadly virus spread by mosquitoes. Particularly in the Indian subcontinent, a large proportion of these cases is associated with the monsoon season and rain patterns. In recent years, there have been abrupt spikes in dengue cases across Indian cities, particularly in western India. To understand this complex interaction of viral proliferation and local environmental conditions, the last ten years of dengue case patterns have been scanned in parallel to the climate data. Our findings suggest that stable temperature windows and humidity levels above certain thresholds trigger a rise in dengue cases. While stable temperature ranges trigger dengue cases, humidity drives such episodes explosive phases and sustainability. Our work pinpoints specific temperature-humidity combinations and suggests that local municipal corporations use them as warning indicators to initiate preventive measures.

Wastewater-based surveillance (WBS) is increasingly used to monitor infectious disease dynamics, yet most evaluations focus on correlation or forecasting - neither of which directly assesses whether wastewater signals can identify the epidemiological events most relevant to public health decision-making. We argue that outbreak onset and epidemic peak detection are the operationally critical use cases of WBS, requiring a fundamentally different evaluation framework. We introduce a classification-based framework that treats WBS as an event-detection problem, defining outbreaks and peaks as discrete events, establishing detection intervals to account for timing uncertainty, and incorporating censoring and data completeness criteria for valid comparisons against imperfect clinical reference outcomes. Within this framework, we apply a Bayesian exponential growth model for outbreak detection - benchmarked against a standard reproductive number (Rt)-based method - and a rule-based algorithm for peak detection, evaluating performance via sensitivity and positive predictive value (PPV). Applied to county-level SARS-CoV-2 wastewater data from 281 U.S. counties (Biobot, 2021-2024), the exponential growth approach substantially outperforms the Rt-based baseline: sensitivity 0.82 and PPV 0.64 versus sensitivity 0.58 and PPV 0.19 for the best-performing Rt variant. Peak detection achieves sensitivity 0.84 and PPV 0.70 at the county level. Both peak and outbreak detection achieve strong and consistent performance against hospitalizations and deaths at the state level. Spatial aggregation yields a statistically significant improvement in peak detection PPV against a curated reference standard ($p < 0.001$), while outbreak detection improvements under aggregation are directionally consistent but not statistically significant. Wastewater leads case-defined outbreaks by 4-6 days but minimally leads epidemic peaks, consistent with wastewater approximating prevalence rather than incidence. These findings demonstrate that wastewater signals can reliably detect outbreak onset and epidemic peaks across spatial scales and clinical outcomes, and that the choice of detection method matters substantially in practice. The classification framework developed here provides a reusable and principled tool for evaluating any surveillance signal as an event-detection system, with direct relevance to how WBS is actually used in public health decision-making.

While tree pollen is a major trigger of allergic respiratory conditions and different taxa exhibit varying allergenic potentials, the lack of high-resolution, taxon-specific exposure metrics have limited our ability to identify which local pollen taxa are primarily responsible for respiratory illness. Traditional pollen monitoring networks, which have an intermittent sampling schedule, are not ideal for examining the delayed effects of pollen exposure due to the missing days. In this study, we developed a modeling framework integrating atmospheric dispersion effects, taxa-specific phenology, and machine learning to predict daily counts of 13 tree taxa in the five-county Metro Atlanta area, Georgia at a 1-km resolution from 2020 to 2024. Machine learning model performance was validated with daily pollen counts collected by a multi-site monitoring network equipped with automated pollen sensors. Findings showed that Betula and Quercus pollens exhibited higher predictive performance than other taxa, with R2 values ranging from 0.69 to 0.92 and from 0.71 to 0.89, respectively. Our 1-kilometer prediction data provides gapless exposure metrics to understand the spatial and temporal variability in pollen exposure, can facilitate investigation of urban pollen hotspots and support epidemiologic studies of pollen-related respiratory outcomes.

Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.

Background: Nitrogen dioxide (NO2) is a surrogate for traffic and industrial air pollution associated with adverse respiratory outcomes. Whether elevated NO2 and temperature jointly influence adult-onset asthma (AOA) risk is unclear, especially among subgroups with varying lifestyle and exposure profiles. We investigated further in the prospective All of Us research program. Methods: Among 596,926 U.S. participants who consented to electronic health record release, annual average NO2 concentrations from satellite data were linked to residential locations for 376,535 individuals. We used multivariable Cox regression to estimate associations between NO2, temperature, and incident AOA, adjusting for co-pollutants and potential confounders. We analyzed 4-category cross-classification variables between NO2 (high>75th percentile vs. low<=75th percentile) and maximum or average temperature (high>median vs. low<=median). We also stratified by sex, age, income, and smoking status. Additive interactions were estimated using Relative Excess Risk due to Interaction, Attributable Proportion, and Synergy Index. Results: We identified 10,413 incident AOA cases over an average 4-year follow-up. Participants with the highest categories of NO2 and temperature exposure had significantly higher risk compared to those with the lowest (HRHigh NO2 x High Max. Temp.=1.37, 95%CI:1.26-1.49; HRHigh NO2 x High Average Temp.=1.49, 95%CI:1.38-1.61). The joint association of high NO2 and high maximum temperature was more pronounced among ever-smokers (HR=1.59, 95%CI:1.40-1.81) than never-smokers (HR=1.26, 95%CI:1.13-1.41). Interaction analyses supported super-additive interactions of high NO2 and high average temperature on AOA risk, particularly among ever smokers, lower-income participants, and younger adults. Conclusion: Our findings highlight the respiratory health threat of long-term joint exposure to elevated NO2 and average temperature, particularly among vulnerable subgroups.

BackgroundCase-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. MethodsWe analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. ResultsTest-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman {rho} = 0.849 vs. 0.679; linear R{superscript 2} = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions ({rho} = 0.924 vs. 0.604; R{superscript 2} = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R{superscript 2} 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. ConclusionsWastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

Background: Wastewater and environmental surveillance (WES) expanded rapidly during the COVID-19 pandemic and is increasingly proposed for routine public health use across a broader range of pathogens. Yet empirical evidence on how decision-makers judge when WES is actionable, how it integrates with existing surveillance, and how its role varies across resource and epidemiological contexts remains limited. Methods: We conducted three structured tabletop exercises (TTXs) at regional Global Wastewater Surveillance Consortium (GLOWACON) meetings in Singapore, Ethiopia, and Panama between March 2024 and May 2025, engaging more than 1,100 participants from over 60 countries spanning public health, government, research, industry, and international organisations. Standardised scenarios and decision prompts, covering respiratory, contact-transmitted, and vector-borne pathogens across multiple outbreak phases, elicited how participants prioritised, implemented, and responded to WES. Data from structured observation notes, participant worksheets, and post-exercise surveys were systematically analysed using a thematic qualitative approach to identify cross-cutting decision patterns and context-specific considerations across regions. This working paper has not been peer reviewed. Findings: Four cross-cutting decision patterns emerged. First, WES was most actionable when it addressed defined surveillance gaps, particularly during early outbreak phases when clinical testing was limited or delayed. Second, decisions to initiate, scale, or de-escalate WES depended on disease severity, the availability of actionable interventions, and the completeness of existing surveillance, not on pathogen type. Third, participants consistently treated WES as complementary to, not a substitute for, clinical and epidemiological surveillance, with its role evolving over the course of an outbreak. Fourth, implementation considerations, including sewer infrastructure, resource constraints, tourism, and mass gatherings varied substantially by setting, while governance, data-sharing, and trust concerns recurred across all three regions. Interpretation: The value of WES is determined less by pathogen-specific characteristics than by how it is embedded within decision-making frameworks in public health systems. These findings provide empirical evidence on how WES is operationalised across diverse global contexts and underscore an urgent need for clearer governance, integration, and prioritisation frameworks without which WES risks remaining an underutilised or inconsistently applied tool despite its demonstrated potential to strengthen pandemic preparedness and response. Funding: This working paper was independently initiated and conducted within the Center on AI, Security, and Technology using income from operations and gifts and grants from philanthropic supporters. A complete list of donors and funders is available at www.rand.org/CAST. RAND clients, donors, and grantors have no influence over research findings or recommendations.

O_LIRapid and ongoing anthropogenic habitat modification has the potential to alter the species composition, abundance and activity of biting insect communities, which are important disease vectors. The resulting changes in the network of interactions between biting insects and their hosts have implications for the transmission of vector-borne pathogens. C_LIO_LIWe used DNA metabarcoding of Diptera blood meals to document bipartite networks of interactions between biting flies (Diptera) and their hosts (including humans, domesticated and wild animals) across a gradient of anthropogenic habitat modification (village, agricultural and near-natural habitat) surrounding two rural villages in Ghana. C_LIO_LIWe collected 7,095 biting Diptera (of 42 species) from 30 collection sites, and generated sequencing data from 75 blood meals (from 29 species). These blood meals contained DNA from 18 vertebrate host species, dominated by humans and their livestock. C_LIO_LIHabitats with lower levels of anthropogenic modification had higher richness of biting Diptera and their host species. Species diversity and evenness did not differ significantly among habitats. Less modified habitats had higher network specificity, but connectance was highest in heavily modified habitats. C_LIO_LIHumans were highly embedded within biting Diptera-host networks, detected in 68% of blood meals. The networks reveal several potential disease transmission pathways linking competent vectors with susceptible hosts. The presence of mixed blood meals containing DNA of both human and wild animal origin highlights the potential for transmission of established and emerging zoonotic disease via bridge vectors. The high betweenness-centrality within interaction networks of the important disease vector Culex watti, combined with its high abundance across all levels of anthropogenic landscape modification, suggest that it may be a connector species, linking and facilitating disease transmission between spatially distinct communities. C_LIO_LISynthesis and applications: Our results are of epidemiological interest, as they identify the exposure of humans to pathogen transmission cycles across a gradient of anthropogenic habitat modification through the movement of opportunistic bridge vectors. We discuss the implications for the transmission of emerging and established zoonotic disease and for the targeting and implementation of initiatives to reduce disease exposure and transmission. C_LI

Objectives To explore the prevalence, spatial aggregation, and demographic correlates of climate change awareness among adults in Nigeria, as well as impacts on humanitarian health preparedness. Design Nationally representative cross-sectional survey with multivariate logistic regression and Global Moran's I and LISA techniques of spatial autocorrelation analyses was applied. Setting All 36 states and the Federal Capital Territory, Nigeria. Participants 1,600 adults drawn from the Afrobarometer Round 9 nationally representative survey. Interventions None. Main Outcome Measures Prevalence, spatial aggregation, and demographic correlates of climate change awareness among adults in Nigeria, and impacts on humanitarian health preparedness. Results Less than one in three Nigerians (30.1%) was aware of climate change, significantly lower than the 65% found in the continent, and education is the most predictive factor, with tertiary-educated Nigerians more than ten times more likely to be aware of climate change than those with no formal education. Most critically, the poor performance in government climate policies is not found in low-awareness states, but in two geographically distinct risk corridors based on a different mechanism and requiring a different policy response. Conclusions The finding shows that the gap in climate awareness is not a communication problem, it is a structural problem - one that requires a national intervention to reduce and close, but that might not be enough because of educational inequality, gender disparity and geographic marginalization. To prepare the country for humanitarian needs, targeted state-level, gender-responsive programming based on Nigeria's Climate Change Act 2021 is required, and effective intervention to make adaptation to the health impacts of climate change happen will need to start with triggering awareness into adaptive health action before climate hazards surpass the country's humanitarian response capacity. Registration Not applicable. Keywords: Climate change awareness; spatial autocorrelation; humanitarian health preparedness; educational inequality; Nigeria

Background Foodborne diseases cause substantial global morbidity and mortality, yet remain largely unattended. To support countries to address this public health concern, the World Health Assembly Resolution 73.5 called for strengthening global food safety efforts and led to the development of the WHO Global Strategy for Food Safety 2022-2030, adopted at the 75th WHA (2022). To this end, the World Health Organization (WHO) reconvened the Foodborne Disease Burden Epidemiology Reference Group (FERG) to advise and support the work to generate updated global, regional, and national estimates of the foodborne disease burden for the reference period 2000-2021. Methods We developed an incidence-based framework expanding coverage to 42 foodborne hazards. Standardized systematic reviews, Global Health Estimates and Global Burden of Disease envelopes, and United Nations population data informed the evidence base. Missing epidemiological data were imputed using Bayesian hierarchical meta-regression models. Disease models mapped acute and chronic health outcomes, applying updated disability weights, life tables, and probabilistic Monte Carlo calculations to estimate incidence, mortality, Years Lived with Disability, Years of Life Lost and Disability-Adjusted Life Years for all 194 WHO Member States. Transparency and analysis reproducibility were ensured through availed open-source R packages and standardized workflows. Results The computational framework provides annual, country-level estimates with improved internal consistency and an expanded hazard scope compared with the WHO 2015 edition. Advances include refined modelling, enhanced uncertainty propagation, and broader inclusion of microbial, parasitic, and chemical hazards. Persistent data gaps---especially in high-burden regions---were filled through extensive imputation. Conclusions The computational framework for the WHO 2026 edition delivers the most comprehensive and transparent assessment of the global burden of foodborne diseases to date. Despite remaining limitations, it enables routine monitoring, supports evaluation of global food safety efforts, and highlights priorities for strengthening national data systems.

Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental covariates. Performance was evaluated using 10-fold outer cross-validation with held-out test folds; R2 was the primary metric. Results The analytic sample comprised 2,414 OECD TL3 regions across 38 countries, for which 939,959 satellite image tiles were processed. In paired bootstrap comparisons, adding satellite features to country indicators improved predictive performance for all outcomes, with incremental R2 ranging from 0.097 to 0.233. The final contextual model achieved R2 values of 0.78 (95% CI, 0.74-0.81) for crude mortality, 0.87 (0.84-0.89) for age-adjusted mortality, 0.86 (0.82-0.88) for infant mortality, and 0.76 (0.69-0.84) for life expectancy. In SHAP analyses, the aggregated satellite image effect consistently ranked among the top predictors across outcomes. Conclusion Satellite imagery captures subnational environmental heterogeneity relevant to regional mortality and life expectancy beyond country identity alone. Earth observation may therefore provide a scalable, complementary data source for characterizing geographic disparities in population health.

Background Childhood overweight and obesity represent major public health challenges, shaped by socio-economic and environmental factors. This study investigates the mediating and moderating role of urban environmental exposures in socio-economic disparities in childhood excess weight. Methods Data was drawn from a population-based sample of children (2-9 years) and adolescents (10-17 years) living in Geneva, Switzerland. Parents reported household financial situation and children's height and weight, from which excess weight (i.e. overweight or obesity) was derived. Residential exposures to air pollution (PM2.5, NO2), noise (daytime, nighttime), and neighborhood greenness (green areas, canopy coverage) were estimated based on geocoded residential addresses. The association between household financial situation and excess weight was evaluated, as well as the mediating and moderating roles of urban environmental exposures. Results The analysis included 1006 children and 1154 adolescents. Among children, an average-to-poor household financial situation was associated with higher odds of excess weight in children (adjusted odds ratio [aOR]: 1.79, 95% confidence interval [CI]: 1.13; 2.84). Higher noise exposure was associated with excess weight (daytime: aOR: 1.40, 95% CI: 1.10; 1.77, nighttime: aOR: 1.37, 95% CI: 1.08; 1.74), while the association with PM2.5 appeared stronger among socio-economically disadvantaged children, though the interaction did not reach statistical significance (financial situation x PM2.5 interaction: aOR: 1.59, 95% CI: 0.98; 2.59). No significant associations were observed among adolescents. Conclusion These findings highlight the joint influence of social and environmental inequalities on childhood excess weight and stress the need to address these interconnected determinants to design equitable, targeted public health interventions.

Introduction: Socioeconomic deprivation is often associated with poorer health outcomes, but some studies suggest the opposite for Lyme disease. Here we test two hypotheses to explain this: differences in (i) local landcover of high risk habitats such as woodlands (landscape hypothesis) and (ii) outdoor recreation in such habitats (behaviour hypothesis). Methods: We analysed reported Lyme disease incidence data for 824 data zones in the city of Glasgow, UK, against deprivation rank (based on indicators relating to income, employment, health, education, crime and housing). We then tested how these relate to woodland cover and indices of urban greenspace usage (per capita and per ha of greenspace). Additionally, we measured Lyme disease hazard (density of infected ticks) in 32 greenspaces and tested relationships with deprivation, woodland and greenspace usage. Results: More advantaged data zones (data zones with low deprivation rank) had higher Lyme disease incidence. These areas had more woodland and woodland cover was positively correlated with both Lyme disease incidence and hazard. Deprivation did not correlate with greenspace usage, nor did greenspace usage correlate with Lyme disease incidence. Intensely used greenspaces had lower infected tick densities, consistent with a human disturbance effect on wildlife that carry ticks. Conclusions: Differences in woodland cover, but not outdoor recreation behaviour, can help explain our finding of higher Lyme disease incidence in more advantaged areas. However, to further test the behaviour hypothesis, we need more detailed data on outdoor recreation activity per capita both locally and in rural areas, as well data on mitigation behaviours.

Introduction Air pollution is the largest environmental risk to human health. Children are disproportionately affected by air pollution and their exposure is amplified during physical activity. Observed concentrations of nitrogen dioxide in 1 in 4 London school playground exceeds the European limit, but the health impacts of air pollution exposure in London school playgrounds remain unexplored. Our study aims to assess and compare the acute changes in lung function and airway inflammation of primary school-aged children exercising in school playgrounds. Methods and analysis 330 children aged 8 to 11 years from ten London schools will be recruited to complete 90 minutes of physical activity and 90 minutes of rest in their school playground in a randomised crossover design. Pre-, post-, and 24-hour post-exposure oscillometry measurements will be performed with airway resistance at 5 Hz (R5) the primary physiological outcome. Nasal lavage samples will be collected pre-exposure and 24-hour post-exposure for analysis of inflammatory, oxidative, and vascular biomarkers, with IL-6 as the primary biological outcome. Mixed-effects regression models will examine associations between estimated pollutant exposures, exercise and physiological responses.

Background: Dengue is a major public health problem in Brazil, and Minas Gerais is one of the states with the highest burden. In January 2019, the Brumadinho dam collapse released about 12 million cubic meters of iron ore tailings into the Paraopeba River basin, causing environmental disturbance that could plausibly affect vector habitats and dengue transmission. We evaluated the spatiotemporal dynamics of dengue in Minas Gerais from 2014 to 2023 and tested whether the disaster was associated with changes in affected municipalities. Methods: We performed an ecological spatiotemporal analysis using dengue notifications from SINAN for all municipalities in Minas Gerais (2014-2023). Municipalities were classified as Paraopeba basin, regional controls, or state controls. Temporal similarity was assessed using Pearson correlation-based hierarchical clustering and non-metric multidimensional scaling (NMDS). Sources of variation were examined with PERMANOVA and principal component analysis (PCA). A linear mixed-effects model with municipality as a random effect was used to test changes after 2019, with pre/post contrasts estimated from marginal means. Results: Dengue showed strong temporal synchrony across the state, with major epidemic peaks in 2015-2016, 2019, and 2023. Health region explained 31.5% of the variation in temporal incidence profiles (p = 0.001), whereas Paraopeba basin status explained no significant variation (p = 0.998). No temporal cluster was enriched for municipalities in the Paraopeba basin. PCA identified 2023, 2019, and 2016 as the main years driving variability. In the mixed model, year was significant (p < 0.001), but Paraopeba basin status and its interaction with time were not. Incidence increased significantly after 2019 in non-exposed municipalities (p < 0.001), but not in basin municipalities (p = 0.088). Conclusions: Dengue dynamics in Minas Gerais were driven mainly by regional and state-wide epidemic processes, with no significant independent effect of the Brumadinho dam collapse on notified dengue patterns.

Gold is widely distributed in the biosphere, and higher plants growing on geochemically anomalous substrates can accumulate significant amounts of gold. This study reports, for the first time from Goa, the detection, spectroscopic characterisation, and X-ray diffraction analysis of phytoformic gold -- biologically sequestered crystalline gold -- in the above-ground dry litter ash of six tree species (Acacia auriculiformis, Alstonia scholaris, Anacardium occidentale, Artocarpus heterophyllus, Ficus benghalensis, Syzygium cumini) growing on mining dumps within the North Goa Banded Iron Formation (BIF) Belt of the Western Dharwad Craton. Microgravimetric analysis of aqua regia-extracted heavy ash fractions revealed gold concentrations of 275-1100 ppm, two to five orders of magnitude above the crustal background ([~]0.004 ppm). Fourier Transform Infrared (FTIR) spectroscopy of 0.22{square}m membrane-filtered crude extracts confirmed the tetrachloroaurate(III) complex [AuCl{square}]{square} as the dominant dissolved gold species, with the diagnostic 1400-1700{square}cm{square}1 absorption envelope present in all six species. UV-Visible spectrophotometry confirmed chloroauric acid formation with a universal {lambda}max at 372.5{square}nm across all species. Powder X-ray diffraction (XRD) of heavy ash fractions yielded the characteristic FCC metallic gold reflections Au(111), Au(200), and Au(220) in all five species analysed. Application of the Debye-Scherrer equation to the Au(111) reflection (2{theta} = 38.2{degrees}, Cu K) established crystallite sizes of 17.7-31.8{square}nm, confirming that phytoformic gold exists as nanoscale crystalline particles in all species. Ficus benghalensis produced the largest and most crystalline gold nanoparticles (31.8{square}nm) and uniquely exhibited strawberry-shaped isomorphic auriferous siliceous biominerals designated phytoauroliths. The described low-cost protocol -- ashing, aqua regia extraction, membrane filtration, and multi-technique spectroscopic and diffraction confirmation -- constitutes a validated method for rapid biogeochemical gold anomaly detection. Applications in gold phytoextraction and mining waste phytoremediation are discussed.

Respiratory virus surveillance is often constrained by symptomatic testing and centralized sampling, producing blind spots in populations at risk. We developed a transdisciplinary community science framework in which students and teachers co-designed and implemented decentralized respiratory surveillance integrating environmental, molecular, and social data in schools. Within this participatory setting, indoor air COLJ concentrations were monitored alongside student-collected air filter and surface samples, analyzed by digital PCR and sequencing. Community-generated samples reliably captured circulating viruses, while COLJ measurements revealed associations between indoor air quality and pathogen abundance. Quantitative surveys identified social and structural barriers to preventive measures, and shared ownership of study design fostered sustained engagement. By embedding epidemiology within a co-developed, community-driven research process, this study demonstrates how scalable surveillance can close critical data gaps. Our work provides a blueprint for decentralized, community-engaged infectious disease monitoring that positions local partners as active contributors to public health intelligence.

INTRODUCTION: Alzheimers disease and related dementias (AD/ADRD) arise from cumulative environmental, social, behavioral, and biological influences across the life course. The neural exposome framework conceptualizes how exogenous, behavioral, and endogenous factors interact to shape brain health; however, its application to preclinical AD/ADRD research, particularly in rural populations, remains limited. METHODS: We developed and piloted a community-embedded, decentralized research model to operationalize the neural exposome framework among cognitively unimpaired adults aged 45+ in two rural Midwestern U.S. communities, integrating environmental, social, behavioral, geospatial, and biological measures to evaluate exposure-related neurobiological and cognitive vulnerability. RESULTS: This approach demonstrated high feasibility and acceptability, achieving strong recruitment, retention, data completeness, and multidomain biomarker collection in rural community-based settings DISCUSSION: Pilot findings support the feasibility of neural exposome-informed research in rural U.S. communities and highlight its potential to advance prevention-oriented research on brain health and AD/ADRD.