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.

Coccidioidomycosis (Valley fever) is a soilborne mycosis endemic to the US Southwest whose incidence has increased markedly in recent decades. Environmental conditions are thought to influence the soil-dwelling lifecycle of Coccidioides; however, most prior studies have relied on above-ground meteorological conditions - primarily precipitation and air temperature (AT) - with few examining subsurface soil moisture (SM) and soil temperature (ST), which may more directly influence the fungal lifecycle. No study of coccidioidomycosis, or any other environment-sensitive soilborne mycosis, has examined how deeper-layer soil conditions relate to disease incidence, despite the prevailing soil-sterilisation hypothesis implicating deeper soil as a potential fungal refugium. Furthermore, nonlinear exposure-lag-response relationships for key dust-dispersion exposures - including PM10, a potential proxy for airborne spore concentration, and wind speed - remain uncharacterised. We aimed to estimate and compare the associations between coccidioidomycosis incidence and environmental exposures across multiple above- and below-ground layers, and to evaluate their independent and combined predictive performance. This ecological time-series study analysed 185,486 reported cases of coccidioidomycosis in Arizona's hyperendemic tri-county region (Maricopa, Pima, and Pinal) during 1997-2024. We developed a mechanism-informed multilayer environmental framework comprising one dust-dispersion layer (PM10, wind speed) and four soil-climate layers - meteorological (precipitation, AT), topsoil (0-10 cm), midsoil (10-40 cm), and deepsoil (40-100 cm) SM and ST. We fitted distributed lag non-linear models (DLNMs) with season-specific interaction terms to estimate exposure-lag-response associations between each environmental layer and coccidioidomycosis incidence. We then developed a two-stage stacked ensemble machine learning framework to assess each layer's independent predictive performance (stage 1) and integrate them into a unified forecast (stage 2), which was evaluated using a strictly held-out test period. At concurrent lags (1-3 months prior to reporting), coccidioidomycosis incidence was primarily associated with dustier, windier, and drier conditions, cooler air temperatures, and warmer topsoil. For each IQR increase, PM10 showed the most consistent concurrent associations, with significant positive incidence rate ratios (IRRs) across all four incidence seasons at lags 1-2 (ranging from 1.04 [95% CI 1.00-1.08] to 1.37 [1.26-1.48]). Across lags 1-36 months, all four soil-climate layers exhibited nonlinear, non-monotonic, and season-dependent associations with coccidioidomycosis incidence, characterised by alternating wet-dry and cool-warm oscillations. Topsoil displayed the most frequent significant associations, with moisture-temperature signals attenuating progressively from topsoil through midsoil to deepsoil. A depth-dependent lag structure was observed for both moisture and temperature, in which significant positive IRRs emerged at progressively shorter lags with increasing soil depth, accompanied by vertical divergence across depths at the same lag windows. For example, for fall incidence, positive moisture IRRs appeared at precipitation lag 15 (1.10 [1.00-1.21]), topsoil SM lag 9 (1.22 [1.15-1.30]), midsoil SM lags 8-9 (up to 1.23 [1.11-1.37]), and deepsoil SM lags 4-5 (up to 1.08 [1.01-1.16]); at these same lags, deepsoil SM was positively associated with incidence whereas topsoil SM and precipitation remained negatively associated. During the held-out test period (2021-2024), the multilayer ensemble generally captured seasonal and interannual variation well, including the timing and approximate magnitude of most peaks, outperforming all single-layer models. Although individual layers had slightly lower test RMSEs, their test gap ratios were substantially higher (0.27-0.67 vs 0.00), indicating that the ensemble generalised far more reliably. All five environmental layers contributed to the final ensemble forecast; the dust-dispersion and topsoil layers received the highest importance, with PM10 ranked as the most important predictor group. The best-performing of four pipeline configurations relied solely on environmental inputs available within one week, enabling the model to function as a near-real-time nowcast. This study provides the first evidence linking multilayer environmental exposures to coccidioidomycosis incidence across both temporal and vertical dimensions, offering new quantitative support for the prevailing soil-sterilisation and grow-and-blow hypotheses and demonstrating that a multilayer framework could improve both mechanistic understanding and predictive performance. The framework could be generalised to other endemic settings and readily extended with new data and methods to inform surveillance and public health preparedness. These findings support incorporating multilayer lagged environmental exposures into both effect estimation and forecasting systems to better prepare endemic regions for anticipated warming, drying, and increasingly variable climatic conditions.

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.

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.

Tick-borne disease risk reflects the combined influence of vector abundance, pathogen prevalence, human exposure, and seasonal activity, yet these components are rarely integrated at the county level. We developed species-specific ecological risk scores for three medically important tick species (Acari: Ixodidae): Amblyomma americanum (L.), Dermacentor variabilis (Say), and Ixodes scapularis Say, across all 102 Illinois counties using data from 2019 to 2022. County-level predicted tick abundance and pathogen prevalence were scaled and combined using equal weights to generate primary vector-level risk scores. Robustness to the equal-weight assumption was evaluated by systematically varying the relative weights assigned to abundance and pathogen prevalence, while a separate sensitivity analysis incorporated reported tick-bite incidence where available. Associations with human disease were evaluated using negative binomial regression of tick-borne disease case counts on the corresponding risk scores, with county population included as an offset. Monthly phenology indicators were constructed by integrating human disease cases, reported tick bites, and sampled tick abundance and were evaluated in relation to temperature and precipitation. Seasonal risk was estimated by combining county-level vector risk with species-specific monthly phenology, generating monthly risk surfaces that were deployed through an interactive R Shiny application. Higher risk scores were significantly associated with greater disease case rates for each tick species. County rankings were highly stable under moderate changes in abundance and prevalence weighting. Seasonal activity differed among species, with A. americanum and D. variabilis showing concentrated warm-season risk and I. scapularis exhibiting a broader seasonal profile. These county-level seasonal risk maps identify where and when relative tick-borne disease risk is elevated in Illinois, supporting targeted prevention, surveillance, and clinical awareness.

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.

Background. Naegleria fowleri, the causative agent of primary amoebic meningoencephalitis (PAM), is a thermophilic free-living amoeba historically endemic to warm freshwater environments, particularly in the southern United States. While human infections remain exceedingly rare, they are almost uniformly fatal. Global climate change is hypothesized to expand the geographic range of thermotolerant pathogens, potentially introducing them into previously unaffected regions. Objective. This study aims to quantify the current and future habitat suitability for N. fowleri within the contiguous United States (US) and, critically, to project these models onto Europe, a continent traditionally considered low-risk, to identify regions of potential emergence under climate change scenarios. Methods. We employed a modeling approach using the machine learning algorithm Maxent, implemented via the `flexsdm` R package. We utilized 204 occurrence records from the contiguous US. We evaluated three environmental datasets (ENVIREM, Global Cloud Dynamics, Worldclim v.2), selecting Worldclim v.2 for its superior predictive performance. Future projections were generated for the 2041-2060 period under the Shared Socioeconomic Pathway (SSP) 2-4.5 (intermediate scenario). Variable importance was assessed using Shapley additive explanations (SHAP) to decouple the contribution of each Bioclimatic predictor and identify the key drivers of range expansion. Results. The Worldclim v.2 dataset provided the most robust model. The model accurately identified the southeastern US as the core current habitat, consistent with historical PAM case distribution. Crucially, when projected onto Europe, the model reveals extensive regions of currently low suitability that transition to moderate or high suitability by 2050, particularly across the Mediterranean basin, parts of Central Europe, and the Atlantic coastal fringes. SHAP analysis identified temperature-related variables, specifically Annual Mean Temperature, Mean Temperature of the Coldest Quarter, and Mean Diurnal Range, as the most influential predictors, followed closely by precipitation variables (Precipitation of Driest Quarter/Month and Precipitation Seasonality). Under the SSP2-4.5 scenario, the model projects a distinct northward and eastward expansion of suitable habitat, with Ukraine emerging as a region of particular concern, especially in light of recent anthropogenic hydrological changes to the Dnipro River basin. Conclusion. Our findings support the hypothesis that global warming will facilitate the geographic spread of N. fowleri, carrying this pathogen into uncharted European waters. The projections for Europe, and for Ukraine, in particular, suggest a non-negligible risk of future autochthonous PAM cases. These results warrant the proactive inclusion of this pathogen in public health surveillance programs and climate change adaptation strategies across the continent.

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.

Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.

Lassa fever is a major yet persistently neglected viral hemorrhagic disease in West Africa, with an estimated 100,000-300,000 infections and approximately 5,000 deaths reported annually across the region. Case-fatality rates are typically above 15% among hospitalized patients and may approach 50% during epidemic periods, with Nigeria accounting for the largest reported burden of confirmed cases. This study presents a pilot multi-layer GIS framework for the spatial recording, visualization, and analysis of hemorrhagic fever infections in Nigeria, focusing primarily on Lassa fever. The framework integrates epidemiological observations with environmental information to move beyond conventional static disease maps toward dynamic assessment of disease susceptibility. Confirmed Lassa fever cases recorded between 2023 and 2025 were combined with weather variables obtained from Open-Meteo, CHIRPS precipitation data, land-use information, lithological characteristics, and elevation datasets. The datasets were temporally aggregated and statistically analyzed, while multiple GIS layers were used to identify and visualize recurring spatial patterns. Ondo, Edo, Bauchi, and Taraba consistently emerged as prominent hotspot states during the study period. Weekly temporal aggregation was adopted to correspond with the reporting frequency of official epidemic surveillance, enabling short-term environmental conditions to be examined in relation to confirmed cases. The findings indicate a pronounced seasonal pattern, with increased Lassa fever activity occurring predominantly during dry periods characterized by minimal rainfall, fewer precipitation hours, and reduced humidity, particularly between December and March. Of the 28 environmental variables examined, precipitation- and humidity-related indicators demonstrated the strongest inverse associations with weekly confirmed cases, with Spearman correlation coefficients reaching {rho} =-0.75. Air temperature showed the strongest and most consistent positive association, reaching {rho} = 0.633. These findings demonstrate the potential of integrating epidemiological surveillance and environmental GIS layers to characterize spatial and temporal patterns of Lassa fever and provide a foundation for more dynamic susceptibility mapping and environmental risk assessment in Nigeria.

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.

Understanding the relationship between weather conditions and infectious disease transmission has important public health implications; nevertheless, the functional form of this relationship often remains poorly understood. Humidity has previously been suggested to have a U-shaped effect on the transmission of respiratory pathogens, with optimal transmission occurring at both low and high levels. Here, to assess the impact of this functional form and of its potential misspecification, we first simulate transmission rates based on the U-shaped relationship and then test whether an exponential function accurately estimates these rates across a range of climates. We further use a transmission model to simulate case count data for a range of hypothetical pathogens, comparing outbreak dynamics under the U-shaped forcing function to those under the best-fit exponential approximation. In our simulations, the model fit for the exponential function is climate-dependent, performing best in very dry and very humid environments. In locations where specific humidity values centre around the vertex of the U-shaped function, the exponential model underestimates the amplitude and frequency of incidence peaks. These findings demonstrate the potential consequences of assuming an incorrect functional form for weather-infectious disease relationships and highlight the need for more careful consideration of this functional form in modelling practice.

Vaccine hesitancy remains a major public health challenge, highlighting the need for effective strategies to boost vaccine uptake. We conducted a county-randomized trial to evaluate whether a social media campaign grounded in alerts driven by community-level wastewater surveillance could increase COVID-19 and influenza vaccination. Forty New York State counties were selected for an 11-week campaign during fall 2024, with 20 counties assigned to the intervention and 20 serving as controls. The campaign reached more than 16.6 million impressions, with over 77K clicks on the call-to-action link, demonstrating substantial public engagement. During the campaign period, intervention counties administered significantly more COVID-19 vaccine doses ({beta} = 76.7, P = 0.01) but fewer influenza vaccine doses ({beta}= -140.9, P = 0.04) per 100,000 residents per week than control counties. However, season-level analysis showed no significant differences in COVID-19 or influenza vaccination uptake between groups. These findings highlight the potential of alerts from community-level wastewater surveillance to promote vaccination.

Vegetation plays a critical role in shaping fine-scale tick density by structuring local microclimates, habitat resources, and host interactions that largely determine tick survival and activity, with implications for tick-borne disease transmission. Tick burden in the United States continues to increase, with annual reported tick-borne disease cases more than doubling from 2004 through 2019. These trends call for a broader scope of tick-mitigation strategies that go beyond traditional prevention measures that focus on personal protection; and there is an opportunity to inform habitat-level interventions. Here, we quantify fine-scale vegetation structure across 48 subplots at tick surveillance sites in Western Massachusetts to assess local habitat features associated with Ixodes scapularis density and potential for human-tick contact risk. In assessing understory and overstory vegetation attributes, this study identified life stage-specific differences in predicted tick density, with higher adult densities in areas with deeper leaf litter and presence of invasive plant species and nymphs associated with high presence of ferns. Both adult and nymphal Ix. scapularis were predicted at higher densities in relatively low basal area forests (18.9 to 26.9 m2/hectare). This study design provides a simple, low-cost framework for organizations looking to incorporate tick-informed forest measurement ecology into their public health tick surveillance protocols with minimal training.

Background: Dengue fever has become an epidemiological concern worldwide, with 531,617 cases reported in Argentina during the 2023-2024 season. The El Nino Southern Oscillation (ENSO) phenomenon has been linked to changes in dengue dynamics. This study assessed the relationships among ENSO, local climatic variables, and dengue outbreaks in Argentina from 2018 to 2024. Methods: A nationwide spatiotemporal analysis was conducted using dengue cases and monthly mean temperature, relative humidity, and rainfall. The Southern Oscillation Index (SOI) was used as the ENSO indicator (El Nino phase=< minus0.5, neutral phase= minus0.5 to 0.5, and La Nina phase= > 0.5). We fitted Hierarchical Bayesian models with Integrated Nested Laplace Approximation (INLA). A Negative Binomial distribution of dengue cases was used to account for overdispersion. Spatial dependence was modeled by using the Besag York Mollie 2 (BYM2) structure, adjusted for population. Results: The final model explained 53.7% of the variation in Argentinas departmental monthly dengue cases during the study period. Exposure response curves of posterior probability and 95% credible intervals suggested that dengue risk increased when monthly mean temperatures were between 20 and 30 degrees Celsius during the El Nino or neutral phase, with less consistent effects during the La Nina phase. Humidity >50% was associated with increased dengue risk during the La Nina phase, with less steep or absent associations during El Nino or neutral phases. Negative SOI scores were associated with an increased risk of dengue after controlling for the covariates. Conclusion: Dengue outbreaks in Argentina were independently associated with temperatures between 20 and 30 degrees celsius during the El Nino and neutral phases, humidity > 50% during the La Nina phase, and the El Nino phase of the ENSO. Incorporating ENSO indices into predictive models could enhance early warning systems and timely public health interventions in Argentina.