GeoHealth

Tropical cyclones are among the deadliest and costliest natural disasters in the United States, and the most intense storms are expected to become more frequent as the climate warms. Anticipating where deaths are most likely to occur is therefore central to preparedness, evacuation planning, and public health response. We modeled block-level mortality risk for twenty-four of the deadliest and costliest tropical cyclones to strike the U.S. Gulf and East Coasts, Puerto Rico, and the U.S. Virgin Islands between 1992 and 2024. For each storm, we combined NOAA hazard data (wind swaths, rainfall, and storm-surge inundation) with 2020 U.S. Census demographic and socioeconomic characteristics and the CDC/ATSDR Social Vulnerability Index for all Census blocks within 25 miles of the coast, and trained storm-specific boosted-tree models with population-standardized mortality as the outcome. Averaging block-level predictions within Saffir-Simpson categories yielded risk maps spanning tropical storms through Category 5 hurricanes. Predicted mortality risk rose with storm severity and concentrated in urban coastal communities of Puerto Rico, Louisiana, Florida, North Carolina, Virginia, Maryland, New Jersey, and New York, as well as in low-lying inlet, peninsula, and sound geographies. Large block population, non-Hispanic composition, male-dominated blocks, predominantly white blocks, and males aged 20 to 34 years ranked among the strongest predictors of mortality; patterns that likely reflect structural factors shaping exposure rather than individual susceptibility. The category-specific risk maps and an accompanying interactive dashboard provide a practical decision-support tool for emergency managers, planners, and coastal residents preparing for future storms.

Climate change detection and attribution (D&A) methods have become vital for quantifying the influence of anthropogenic forcing on the Earth's systems, including human health. Health impact attribution (HIA) studies seek to disentangle climate-driven health effects from natural variability yet are often constrained by the availability of accessible counterfactual climate scenarios. This tutorial paper presents a flexible, reproducible framework for developing counterfactual climates without reliance on computationally intensive global circulation models. We provide practical, R-based methodologies for constructing both trend-based (temperature and non-temperature) and event-based counterfactual, using a variety of techniques including model residual detrending, data-driven decomposition (e.g., Singular Spectrum Analysis and Empirical Mode Decomposition) and stochastic weather generators. The tutorial also explores the incorporation of greenhouse gas concentrations as forcing variables, rather than global mean temperature anomalies. By operationalising these methods through worked examples and an open code repository, this paper aims to build capacity within the HIA community, enhance methodological transparency, and foster interdisciplinary collaboration between climate and health researchers.

Hydroclimate whiplash, defined as the rapid transition between unusually wet and dry conditions, is expected to intensify under climate change, yet its population health impacts remain largely unknown. Here we quantified the association between hydroclimate whiplash and mortality across the contiguous United States from 2003 to 2023 using monthly county-level mortality records, standardized precipitation evapotranspiration index data, and two-stage time-series models. We identified overall and direction-specific dry-to-wet and wet-to-dry whiplash events at seasonal and sub-annual timescales and across 5-, 10-, and 20-year recurrence intervals. More severe whiplash events were associated with higher all-cause mortality risk; 5-, 10-, and 20-year sub-annual overall whiplash events increased mortality risk over five months by 3.4%, 4.5%, and 5.7%, respectively. Elevated risks were observed across cause-specific mortality outcomes, with the strongest association for infectious diseases. We estimated that 103,471 deaths were attributable to overall whiplash during the study period. These findings identify hydroclimate whiplash as an emerging climate-related public health threat and suggest that adaptation strategies focused on single hazards may underestimate the health burden of rapid, sequential hydroclimatic extremes.

West Nile virus (WNV; Flaviviridae) and Eastern Equine Encephalitis virus (EEEV; Togaviridae) represent the two most significant mosquito-borne zoonoses in the southeastern United States. While both viruses utilize avian amplifying hosts and mosquito vectors, it is unclear if they occur within distinct ecological niches. This study assessed the cumulative effects of both landscape composition and weather variables on the spatial-temporal distribution of WNV and EEEV outbreaks in Georgia over a twenty-four-year period (2001-2025). We used a modeling framework that directly accounts for both spatial and temporal effects but additionally integrates key EO variables (Earth observation). These environmental effects were investigated using Bernoulli generalized linear mixed-effects models (GLMMs) and executed with an integrated nested Laplace approximation (INLA). Our findings revealed disproportional distribution ranges of Culex quinquefasciatus, Aedes albopictus and Culiseta melanura in highly urbanized Georgian counties such as Chatham, Dekalb, and Fulton. Results showed that WNV transmission was heavily influenced by urban land cover (Posterior Mean: +0.78, 95% CrI: [0.61,0.95]) but negatively associated with lagged precipitation (Posterior Mean: -0.18, 95% CrI: [-0.29, -0.07]), confirming drought-driven amplification. Conversely, EEEV transmission was strongly influenced by wetland cover (Posterior Mean: +1.12, 95% CrI: [0.94,1.30]) and precipitation (Posterior Mean: +0.51, 95% CrI: [0.39,0.63]). These results underscore the need for improved mosquito surveillance across all Georgian counties in the face of growing vector-borne disease risks.

In climate-health impact projection studies, projected impacts from multiple general circulation models (GCMs) are commonly aggregated by reporting the mean of GCM-specific impacts as the point estimate alongside a 95% empirical confidence interval (eCI) constructed from the 2.5th and 97.5th percentiles of the simulated pooled distribution of GCM-specific impacts. This study shows that the eCI generally does not yield the nominal coverage probability for the GCM-ensemble mean and constructs an interval aligned with the estimand. In a simulation study, the coverage of the eCI for the GCM-ensemble mean deviates from the nominal level in both directions, whereas the aligned interval yields coverage near 95% across all considered settings. The exact coverages derived analytically under a location-shift model agree with the simulation results. In a reanalysis of a heat-related mortality projection in London, the eCI is consistently wider. The eCI should be distinguished from confidence intervals for the GCM-ensemble mean; rather, the interval may be better described as a simulation-based approximate prediction interval for a GCM-specific impact under the uniformly randomly selected GCM from the considered GCM set.

The impact of weather on the health of Americans and the American health system is substantial. Using available health and economic data, we developed a data-driven scenario that describes a compounded heat emergency in an archetypal community in the United States. We then characterize the potential human and economic costs of such a heat emergency to demonstrate the widespread impact on health outcomes, health systems, and society.

Mosquito-borne diseases pose a growing public health challenge as climate change reshapes vector population dynamics. West Nile virus (WNV), transmitted between birds and Culex mosquitoes, disproportionately affects Maricopa County, Arizona, one of the nation's highest-burden counties, yet whether models that include weather and avian dynamics improve forecast accuracy remains unclear. Using a 15-year weekly time series of mosquito abundance, mosquito infection prevalence, and human cases, we developed four mechanistic model configurations of varying complexity, from mosquito-human dynamics alone to full models incorporating avian dynamics and weather forcing. We fitted each model to the weekly-observed data, generated probabilistic 1- and 2-week-ahead forecast horizons, and evaluated forecasts against a historical baseline. All configurations fit the data equally regardless of weather or avian dynamics. However, models incorporating both birds and weather created more accurate forecasts of mosquito abundance and mosquito infection prevalence, and all configurations outperformed the baseline for forecasting human cases. Forecast accuracy was highest in summer and fall, and ensemble aggregation sometimes outperformed every individual model, stabilizing predictions across the 15-year record. These findings indicate that avian and weather dynamics are most critical for predicting mosquito-specific data, positioning this framework as a scalable tool for public health planning for WNV surveillance under climate change.

For over a decade, the Shared Socioeconomic Pathways (SSPs) have served as the principal framework for quantitative modeling of the socioeconomic dimensions of global environmental change. The SSP scenarios describe many of the ecological and social processes thought to shape pandemic risk, including the emergence of novel pathogens (accelerated by processes such as deforestation, livestock intensification, and land-use change) and their subsequent spread (mediated by factors such as inequality, human mobility, and health system capacity). However, the SSP framework has not been widely incorporated into pandemic risk assessment. Here, we assess how pandemic risk is embedded in the SSP framework, and find that the framework captures most of the social-environmental drivers of pathogen spillover, and many of the social-economic drivers of pandemic spread and impacts. Because climate change and pandemics share many drivers and risk factors-- including ecosystem degradation, animal agriculture, and weak governance--SSP scenarios characterized by higher barriers to climate adaptation also generally imply lower chances of outbreak containment, and greater pandemic impacts on vulnerable populations. Pandemic risk is therefore lowest in SSP1 and highest in SSP3, but SSP5 shows that frequent spillover and effective containment can coexist. These findings suggest that pandemic risk can be understood as part of a broader polycrisis, linking climate change, biodiversity loss, and global health. We suggest that new scenario extensions, or entirely novel frameworks, will ultimately be needed to capture possible shifts in the global health landscape; however, in the meantime, scenario frameworks from the environmental sciences could be valuable tools for initiatives to quantify future pandemic risks.

Wildfire smoke has increasingly affected air quality across North America, raising concerns about the health effects of fine particulate matter (PM2.5) exposure, including potential impacts on brain health. However, relatively few studies have characterized personal PM2.5 exposure during these events using wearable monitoring. We examined daily personal PM2.5 concentrations during wildfire smoke episodes in southeast Michigan alongside neighborhood outdoor PM2.5 estimates. Four participants (one adolescent and three adults) wore AirBeam3 personal monitors during ongoing studies. Neighborhood outdoor PM2.5 was estimated using the average of three nearest PurpleAir outdoor air quality sensors, and wildfire smoke days were identified using state air quality advisories. Group-level descriptive statistics summarized personal and neighborhood outdoor PM2.5 and self-reported time spent outdoors. Exploratory within-participant analyses quantified associations between neighborhood outdoor and personal PM2.5 concentrations on smoke and non-smoke days. Neighborhood outdoor daily PM2.5 concentrations were higher during wildfire smoke days than non-smoke days (87.6 + 80.2 vs. 12.3 + 7.0 {micro}g/m3). Personal PM2.5 concentrations were more than five times higher during wildfire smoke days (28.2 + 20.0 vs. 5.1 + 4.7 {micro}g/m3) than non-smoke days. Within participants, every 10 {micro}g/m3 increase in neighborhood PM2.5 was associated with 2.3 {micro}g/m3 increase in personal PM2.5 concentrations. Wearable PM2.5 monitoring captured elevated personal exposures while providing individual-level exposure information beyond neighborhood outdoor air quality estimates. These findings demonstrate that wearable monitoring complements neighborhood air quality measurements by capturing individual-level exposure, providing a more comprehensive assessment of real-world wildfire smoke exposure for future studies examining the effects on brain health.

Background and aimsExcess nitrate (NO3-), from fertilizer overuse and intensive agriculture, can pollute water and contribute to greenhouse gas production (nitrous oxide - N2O). Plant metabolites from pastural herbs such as Plantago lanceolata (plantain) can inhibit microbial nitrification of ammonium to NO3- (biological nitrification inhibition - BNI) and change soil nitrogen cycle dynamics (lower potential nitrification rate - PNR). The main aim was to investigate differential plant metabolite expression associated with BNI and lowered PNR in different soil types. MethodsSix plantain cultivars were tested for BNI potential and screened for metabolites that correlated with inhibition of the ammonia oxidising bacterium (AOB) Nitrosospira multiformis. PNR and microbiome change was then investigated in four different New Zealand soils under the plantain cultivar Agritonic and ryegrass cultivar One50. ResultsPNR under plantain was 11 to 41% lower than fallow soil while PNR under ryegrass was 0 to 39% lower. In addition to verbascoside and aucubin, plantain metabolites associated with lower PNR included plantamajoside, riboflavin 3- and 5-sulfate, plantagoguanidinic acid. Chlorogenic acid was associated with lowered PNR under ryegrass. PNR reductions, microbiome structure and the ratio of ammonia oxidising archaea (AOA) relative to AOB was modulated by soil type. ConclusionPlantain and ryegrass lowered the PNR in four different soils and was correlated with metabolites beyond just aucubin and verbascoside. Based on candidate BNI-associated metabolites identified, it was hypothesised that lowered PNR is likely indirect through mechanisms such as chelation and appears to be dependent on both plant physiology and soil physicochemistry.

Objective: Over the past decades, US policies intended to reduce air pollution emissions from electric generating units (EGUs), mobile sources (e.g., cars and trucks), and port activities have been implemented to improve air quality. This study aimed to estimate and compare counterfactual air pollution concentrations (i.e., concentrations that would have occurred without these policies) to observed concentrations, and then evaluate the health impacts of such policies in New York City, Los Angeles, and Atlanta from 2005 to 2019. Materials and Methods: We obtained data on respiratory emergency department (ED) visits and cardiovascular disease ED visits that result in hospitalizations for the three cities from 2005-2019. Daily concentrations of fine particulate matter (PM2.5), criteria gases [carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3)], and 1-in-3-day measured concentrations of PM2.5 components and PM sources estimated using positive matrix factorization were acquired from six monitoring sites in the three cities. To estimate health impacts of selected EGU, mobile, and port policies we estimated: 1) counterfactual daily pollutant concentrations at each of the 6 city-sites; 2) associations between daily pollutant concentrations and rates of cardiorespiratory visits using city-site specific multi-pollutant Poisson models; and 3) the percent of cardiorespiratory visits prevented by the implementation of the selected policies, through applying observed and counterfactual concentrations to the fitted health models. Results: Air quality policies were estimated to reduce ambient pollutant concentrations across the three cities, with median PM2.5 reductions of 27%-62% due to all policies combined during 2005-2019. Changes in criteria-pollutant concentrations associated with the selected policies were estimated to avert 7.1% (95% UI: 5.4%, 8.9%) of respiratory visits in New York City, 2.4% (95% UI: 1.4%, 3.4%) in Los Angeles, and 4.5% (95% UI: 0.8%, 8.2%) in Atlanta. In addition, 2.6% (95% UI: 0.9%, 4.2%) and 1.2% (95% UI: 0.3%, 2.1%) of cardiovascular visits were averted in New York City and Los Angeles, while the estimate in Atlanta did not indicate cardiovascular visits averted. Conclusion: The selected EGU, mobile-source, and port policies evaluated during 2005-2019 were estimated to reduce ambient pollutant concentrations and avert respiratory visits in all three cities and cardiovascular visits in New York City and Los Angeles.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Background Childhood respiratory mortality in China has fallen by over 90% in three decades alongside sustained national warming, yet national long-run evidence on temperature and child respiratory mortality is lacking. Methods We linked Global Burden of Disease (GBD) 2021 mortality estimates for China - lower respiratory infections (LRI), ages 0-19, and asthma, ages 0-24, 1990-2021 - with C-LSAT 0.5 deg gridded temperature data (1990-2019), aggregated nationally and to five climate zones. Four annual indicators (mean temperature, diurnal temperature range, seasonal amplitude, interannual variability) entered regressions of log mortality rates with Newey-West standard errors. A bootstrapped (500 resamples) quadratic model probed the minimum mortality temperature (MMT), with PM2.5-adjusted analyses and future-exposure, permutation, and detrended falsification tests. Results LRI deaths fell by 96.3% (330,194 in 1990 to 12,098 in 2021; 95% uncertainty interval 9,669-14,891) and asthma deaths by 94.9% (3,287 to 167), while mean temperature rose 0.364 deg C per decade and diurnal temperature range narrowed 0.092 deg C per decade. Baseline coefficients were large (mean temperature -1.696, SE 0.174; diurnal temperature range +2.408, SE 0.336; seasonal amplitude -0.162, SE 0.082; interannual variability +2.924, SE 1.514, per 1 deg C in log rate), but the future-exposure test failed and detrending nullified every coefficient: the associations are trend-level, and short-cycle causal effects are not identifiable. Nor was the national MMT identifiable - observed temperature support spans only 6.66-8.13 deg C, and the nominal turning point of 35.84 deg C is an extrapolation artifact (quadratic term p = 0.963). Within the observed range, warming and declining mortality moved in the same direction. Conclusions The 96% decline in childhood respiratory mortality cannot be attributed to warming. China sits on the low-temperature side of the optimum, and the marginal direction of future warming requires stronger designs to establish. The falsification framework offers a discipline for climate-health inference in China.

BackgroundVietnams reported dengue burden has risen roughly five-fold since 1990. Multi-decadal studies linking climate indices to dengue rarely separate genuine year-to-year coupling from a long-term trend the two share. MethodsWe assembled a provenance-preserving national annual dengue series (1990-2025; OpenDengue plus Ministry of Health figures) and correlated it with annual and March-May means of eight tropical sea surface temperature (SST) indices at lags of zero and one year under five trend-correction lenses: raw, linear detrending, first-differencing, socio-demographic-index residualisation and AR(1) prewhitening. FindingsCases rose 3{middle dot}5 percent annually. Seven predictors were significant at zero lag, led by the annual Indian Ocean Basin-Wide index (IOBW; Spearman +0{middle dot}534), but linear detrending removed all. First-differencing preserved six, led by spring IOBW (+0{middle dot}468), the annual Atlantic Multidecadal Oscillation (AMO; +0{middle dot}462) and annual IOBW (+0{middle dot}423); three survived AR(1) prewhitening - annual AMO and annual and spring IOBW. Spring AMO and a lag-1 Tropical North Atlantic signal (-0{middle dot}452) did not, and are hypothesis-generating. El Nino-Southern Oscillation indices failed throughout. InterpretationMost of the apparent association reflects a trend shared by warming oceans and expanding surveillance; we could not demonstrate that climate is the primary driver at this scale. Trend is not the whole story: IOBW and annual AMO persist under trend- and persistence-removing transformations. Because transmission responds to climate over weeks to months, annual averaging smooths the lags through which El Nino acts; these nulls reflect temporal scale, not climate insensitivity; usable predictors will require monthly, province-level models. FundingCenter for Environmental Intelligence, VinUniversity (project VUNI.CEI.FS_0001). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Web of Science and Google Scholar for studies published up to May 2026 linking large-scale climate indices or sea surface temperature to dengue incidence, combining dengue, climate, sea surface temperature, ENSO, teleconnection and time-series terms with Vietnam, without language restriction. Many studies covering two or more decades reported strong correlations between basin-scale indices and national dengue counts. Most, however, relied on raw correlations or a single detrending choice, and rarely tested whether an apparent association reflected genuine year-to-year coupling or merely a shared long-term trend. Added value of this studyMost long-term studies remove the shared upward trend in only one way, or not at all. To our knowledge this is the first study to compare five trend-correction methods on a multi-decadal national dengue record and to read their agreement or disagreement as a diagnostic of which climate signals are real. A signal that appears only before the trend is removed is probably following it; one that persists is more likely real. Applied to a record spanning more than three decades, this comparison separates the two: several widely reported raw correlations weakened once the shared trend was accounted for. Implications of all the available evidenceClimate-informed analyses of multi-decadal data should report at least two trend-correction approaches alongside the raw correlation and treat their disagreement as evidence about where a signal sits, rather than operationalising raw long-span correlations. For Vietnam, the apparent national-scale association is dominated by a shared long-term trend but retains a smaller, robust inter-annual component led by the Indian Ocean and AMO signals; genuine coupling is more likely detectable at monthly resolution and provincial scale, where statistical power and physical mechanism are jointly available. Surveillance systems should retain explicit source provenance, so trend-corrected re-analysis remains possible as records grow.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

Background: Empirical surveillance for Aedes-borne arboviruses is inherently reactive, detecting transmission after it has commenced. For small island settings where dengue and chikungunya circulate sporadically, characterising when and where environmental conditions could support local transmission is critical for preparedness. In Mauritius, Aedes albopictus is the sole primary vector for dengue and chikungunya viruses, but previous suitability assessments have relied on Aedes aegypti parameterisation. Methods: We estimated monthly Index P for dengue and chikungunya across 160 localities in Mauritius from January 2014 to October 2024. Index P, a mechanistic transmission suitability measure derived from the Ross-Macdonald framework that climate-dependent transmission potential attributable to one adult female mosquito. Mean temperature and relative humidity were derived from ERA5-Land reanalysis dataset via Google Earth Engine and incorporated within the Mosquito-borne Viral Suitability Estimator (MVSE) framework. Index P was also parameterised with Ae. albopictus-specific biological priors and virus-specific vector competence values for both dengue and chikungunya. Results: Transmission suitability for both viruses was concentrated within the austral summer (November to April), with near-zero values in winter, below the indicative transmission threshold (Index P [≥] 0.5). Chikungunya exhibited consistently higher, more spatially widespread and longer-lasting suitability than dengue: all districts exceeded the transmission suitability threshold for chikungunya (Index P = 0.71), while median dengue Index P = 0.24, remaining below this threshold, during the same study period. Dengue peak suitability was concentrated in western coastal localities, consistent with the greater thermal sensitivity of its extrinsic incubation period in Ae. albopictus. Conclusions: These findings indicate that dengue and chikungunya have distinct, virus-specific climate-suitability profiles in Mauritius, and should not be treated as interchangeable for preparedness purposes. This provides an important Ae. albopictus-parameterised evidence base for Mauritius, enabling seasonal and geographic targeting of surveillance and vector control ahead of, rather than in response to local transmission.

Environmental changes are reshaping the distribution and seasonal dynamics of vector-borne diseases, with important implications for public health. Tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) cause growing concern in Europe, with rising case numbers and ever-expanding circulation areas. The transmission risk of TBEV and WNV follows characteristic seasonal patterns, driven largely by weather-dependent activity of their arthropod vectors. The relative roles of climate and land-use change on the seasonal dynamics and spread of these diseases and their vectors remain, however, poorly quantified. Here, we assess the spread and phenology of TBEV and WNV in response to historical and future climate and land-use changes across Europe. We developed spatiotemporal species distribution models (SDMs) for the viruses and their primary vector species, generating monthly environmental suitability predictions from the 1970s to 2050s. Virus models incorporated vector suitability as a nested predictor to capture the dependence of virus occurrence on vector presence. To disentangle drivers of observed changes, we applied counterfactual historical simulations, attributing shifts in seasonal transmission risk to climate or land-use changes. Historical attribution results show that land-use changes mainly affected absolute vector suitability, whereas climatic changes drove shifts in seasonal transmission risk. Transmission risk is projected to rise continent-wide for both TBEV and WNV over the coming decades. Further, TBEV is projected to undergo pronounced phenological shifts, with a dominant spring peak and a delayed autumn peak extending into October. Prolonged seasonal transmission windows are projected to create hotspots that both intensify and expand across large regions. Taken together, our findings underscore the need for coordinated transnational efforts to manage the projected health burden of TBEV and WNV across Europe, and support upstream prevention by providing climate-informed guidance on intervention timing and spatial prioritisation.

Introduction The interaction between air pollution and airborne pollen is emerging as a major public health concern, as it may enhance allergic responses and exacerbate asthma at the population level. Available evidence suggests that urban residents experience a higher burden of respiratory allergies than rural populations, potentially due to the combined effects of chemical air pollutants and pollen exposure. Air pollutants may modify pollen characteristics, increase allergen release and potency, and promote airway inflammation, thereby amplifying allergic sensitization and respiratory symptoms. Aims For the first time, the relationship between air pollution, pollen, and asthma and allergies is investigated by simultaneously factoring in spatiotemporal land cover data (urban, agricultural, and forest spaces). Methods We utilized descriptive statistics, Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and spatial analysis to understand the relationship of birch, grass and all taxons count, NO2 and PM2.5 concentrations (micro grams/m3) and land cover type (urban/rural, agriculture, forest) in Bordeaux, Clermont, Marseille, Nancy, Paris, Poitiers, Reims and Strasbourg using prescribed and over the count sales data for asthma (R03) and allergies (R06), from a representative sample of pharmacies (n=12,000), in 2013. Results In 2013, the 8 cities saw total sales of 9,684,577 R03 and 12,344,102 R06 medications, with sales peaks coinciding with high pollen and air pollution. A spatiotemporal and land cover analysis revealed that relationships between these variables are highly context-dependent. For instance, higher R06 sales clustered in areas with medium pollen (taxon deciles 3 to 4, grass 4 to 5, birch 6 to 7), high pollution ("NO" _2 deciles 8 to 10, "PM" _2.5 5 and 8 to 10, "PM" _10 3, 7, 9 to10), high agricultural land, and low forest/urban space. Conversely, some associations did not vary by location, suggesting external influencing factors. Conclusions Our data raise evidence that pollen and air pollution can act synergistically according to the land cover characteristics. Other investigations are needed to confirm the relationship.

Alpha-gal syndrome (AGS) is a tick bite-associated allergic condition induced by the lone star tick (Amblyomma americanum). Illinois had no confirmed AGS case data as of January 2026, when mandatory reporting began under the state's TICK Act, leaving practitioners without data to guide screening or resource allocation. We constructed a county-level proxy risk score using a Bayesian conditional autoregressive spatial model applied to three Illinois surveillance sources from 2019-2022: tick abundance, ehrlichiosis cases, and tick establishment status, all linked by a shared vector. Ehrlichiosis was modeled as a population-adjusted rate, ticks as a relative-intensity index, and establishment status as a fixed ecological component. The combined risk score identified a high-risk cluster in far southern Illinois that remained stable across alternative weighting scenarios. This approach is transferable to jurisdictions lacking direct AGS surveillance and offers a starting point for clinician education pending validation against confirmed case data.

Tropical dryland ecosystems are highly biodiverse and fragmented and are experiencing significant anthropogenic and climatic changes. With increasing extremes in temperature and precipitation, coupled with significant alteration, these ecosystems are at greater risk of increased exposure and vulnerability to climatic change; however, little work has quantified the climatic shifts occurring within these ecosystems globally. Here, we aim to fill this gap by using the ERA-5 reanalysis and CHIRPS precipitation data to quantify changes in essential climatic variables in tropical drylands since 2000. Overall, we find that regional pressures differ, with tropical dry forests, savannas, and shrublands becoming hotter and drier in the Neotropics and parts of the Afrotropics and Australasia. By contrast, the tropical dry forests in the Indomalayan, Oceania, and Nearctic are experiencing hotter and wetter conditions. Globally, though, these ecosystems are experiencing more change than the global average, suggesting they may be approaching tipping points in their resilience, ultimately shrinking the area where they can survive.