GeoHealth

Young children (from birth to 5 years old) are uniquely vulnerable to environmental hazards due to their higher exposure relative to body weight, rapid physiological and neurological development, and strong reliance on caregivers for protection and care. Such risks are often amplified in marginalized communities with socioeconomic disadvantage and limited access to resources. However, widely used indices, such as the Social Vulnerability Index (SVI), the Climate Vulnerability Index (CVI) and the Child Opportunity Index (COI), were not specifically developed for young children and may not capture the combined environmental and socioeconomic risks faced by this age group. To address this critical gap, we developed a county-level Early Childhood Environmental Health Vulnerability Index (EC-EHVI) for the contiguous U.S. using multidimensional indicators within an Exposure-Sensitivity-Adaptive Capacity framework and informed by Bronfenbrenners bioecological model. We identified the underlying drivers and the spatial patterns of the EC-EHVI. Our results showed that the EC-EHVI exhibited the strongest association with county-level young child mortality and explained a larger proportion of spatial heterogeneity compared with the SVI, CVI, and COI. Elevated vulnerability clustered in the Great Plains and Southeastern U.S., where over half of high-risk counties were exposure-driven, and 411 high-high hotspots were identified. The EC-EHVI offers a valuable spatial decision-support tool for designing targeted, place-based interventions and advancing environmental health equity for young children. Plain Language SummaryYoung children (birth to age five) are uniquely vulnerable to environmental hazards. Because their bodies are developing and they consume more air, food, and water relative to their weight, environmental exposures can have severe, lifelong impacts. These risks are often magnified in under-resourced communities. Yet, most existing vulnerability tools were not built with young children in mind, potentially obscuring the combined environmental and social threats they face. To address this gap, we developed a new county-level index to pinpoint where young children are most at risk across the contiguous United States. Our tool integrates data on environmental exposure, community sensitivity, and the resources available to help families cope. When tested, our new index was more strongly linked to young child mortality than several widely used existing measures. We identified major high-risk clusters, particularly in the Great Plains and the Southeastern U.S. This tool can help policymakers and public health officials better target resources and interventions to protect young children and promote environmental health equity. Key PointsO_LIWe developed a county-level Early Childhood Environmental Health Vulnerability Index across the contiguous U.S. C_LIO_LIElevated vulnerability clustered in the Southeast, Great Plains, and Appalachia, with additional hotspots in Michigan and Maine. C_LIO_LIMore than half of high-vulnerability counties were exposure-driven, emphasizing the key role of environmental hazards in child health. C_LI

As climate change intensifies, health risks from extreme heat are rising. Accurate assessment of heat vulnerability at high spatial resolution is crucial for developing effective adaptation strategies, particularly in socioeconomically heterogeneous urban settings. However, the identification of key indicators underlying heat vulnerability remains challenging. Using Chicago, Illinois (USA) as a case study, we systematically compare different variable selection strategies in community-level heat vulnerability assessments. We take the conventional unsupervised principal component analysis (PCA)-based Heat Vulnerability Index (HVI) as a baseline, and compare it with supervised approaches that incorporate variable selection, including machine learning algorithms (Lasso regression, Random Forest, and XGBoost) as well as traditional statistical methods (simple linear regression and polynomial regression). Using the vulnerability indicator subsets identified by each variable selection method, we construct multiple HVIs and evaluate their performance against heat-related excess mortality. Our work indicates that supervised variable selection improves the performance of HVIs in capturing heat-related health risks. Among all methods, the Random Forest-based variable selection algorithm achieves the best overall results, highlighting the potential of machine learning to enhance heat vulnerability assessment tools. Our results demonstrate that poverty rate, lack of air conditioning, and proportion of residents aged 65 and above are robust determinants of heat vulnerability in Chicago.

Understanding how infectious disease spreads through exposure is key to predicting outbreaks and effective control measures. This is important for wildlife, where outbreaks often lead to devastating ecological consequences. Traditional epidemiological models often assume equal infection risk regardless of exposure dose and focus on a single infection stage. This assumption eventually overlook variations in initial doses and the role of environmental pathogen reservoirs. We hypothesized that higher exposure doses accelerate disease progression, increase mortality, and elevate pathogen shedding but reduce infectious periods. To evaluate this, we estimated key disease parameters - including latency period, disease-induced mortality, zoospore shedding rates, and transmission probability-from datasets of eastern newt (Notophthalmus viridescens ) exposed to varying zoospore concentrations of fungal pathogen Batrachochytrium salamandrivorans (Bsal). Guided by the empirical evidence,we developed a novel mathematical model capturing dose-dependent transmission, multiple exposure stages, and environmental pathogen accumulation. This empirically driven modeling framework reveals how exposure levels shape disease trajectory, host outcomes and pathogen persistence. Our findings indicate that high initial doses lead to faster disease progression and higher mortality. On the other hand, environmental transmission can sustain outbreaks even under low direct contact rates. Integrating dose dependence with environmental transmission, our framework advances epidemiological modeling of pathogen persistence.

Global environmental change is reshaping human exposure to zoonotic and environmentally acquired pathogens, yet predicting disease risk remains challenging. High-resolution risk maps typically rely on human case data and environmental correlates, often overlooking ecological processes such as wildlife reservoirs. We evaluated whether mammalian reservoir distributions improve prediction of coccidioidomycosis (Valley fever), an emerging, environmentally-acquired fungal disease with a poorly characterized range. Using county-level coccidioidomycosis notification data from the Centers for Disease Control and Prevention, we developed a hierarchical Bayesian model of county-level endemicity, defined as >=10 cases per 100,000 population. We incorporated climatic, environmental, and vegetation covariates, a state-level reporting effect, and species distribution models for 22 mammalian species previously identified as Coccidioides reservoirs. We found that the number of endemic mammalian reservoirs in a county was the strongest predictor of coccidioidomycosis endemicity, with each standard deviation increase in reservoir species richness associated with substantially higher odds of endemicity (log-odds ratio = 1.702; 95% CI: 1.060-2.419). In contrast, maximum vapor pressure deficit, soil moisture, and land cover were not independently associated with endemicity after accounting for reservoir distributions. State-level reporting effects revealed substantial heterogeneity, and comparison of models with and without reporting effects identified regions likely to be endemic but underreported, including parts of Nevada, Utah, New Mexico, Texas, and Colorado. Our results establish reservoir diversity as a central predictor of zoonotic fungal disease risk and demonstrate a transferable framework for distinguishing between ecological drivers of infection from surveillance bias to improve disease risk mapping and identify areas of potential underreporting.

IntroductionParticulate Matter (PM2.5) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven and is limited in many limited ground monitoring network settings. Road-traffic proxy indicators can provide indirect estimates of PM2.5 where measurements are limited but require context-specific validation. We evaluated three PM2.5 road-traffic related proxies:(I) population-Weighted Road Network Density (WRND), (ii) Euclidean (straight line) distance from highways (EH), and (iii) Euclidean distance from main roads (EM). MethodsWe validated proxies using high-resolution outdoor filtered PM2.5 personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Village-level spatial patterns for the PM2.5-proxy relationship were mapped using 5 km hexagonal aggregated tessellations. Proxy-PM2.5 associations were assessed using Spearman correlation, and predictive utility was tested using country-specific and global Random Forest (RF) models (3-fold cross-validation), reporting R2, RMSE, and feature importance ResultsSpatial mapping showed heterogeneous proxy-PM2.5 relationships across and within sites, with elevated PM2.5 occurring in both low- and high-proxy contests. WRND-PM2.5 correlations were weak overall and statistically significant only in Mozambique (r = 0.351; p = 0.005), with non-significant associations in Kenya (r = -0.041; p = 0.673) and The Gambia (r = -0.020; p = 0.909). EH-PM2.5 correlations were positive in The Gambia (r = 0.335; p = 0.053) and Mozambique (r = 0.292; p = 0.020) but negative and significant in Kenya (r = -0.224; p = 0.018).Single-variable RF models performed poorly across all countries (R2 < 0.45) and the Global model (R2=0.42). Combining proxies improved performance in Kenya (R2=0.52; RMSE=31.7{micro}g/m3) and Mozambique (R2=0.60; RMSE=8.9 {micro}g/m3), Global R2=0.46; RMSE=29.1 {micro}g/m3), although in The Gambia, the combined model (R2=0.53; RMSE=37.6 {micro}g/m3) did not exceed the best single-proxy model. ConclusionRoad-network proxies provide context-dependent signals of personal PM2.5 exposure, and predictive performance is strengthened when proxies are combined in a hybrid model.

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.

Background Ambient air pollution is a leading global health risk and disproportionately affects populations of Low- and Middle-Income Countries (LMICs). In 2021, WHO revised its Air Quality Guidelines (AQG), lowering recommended annual limits for Particulate Matter 2.5 (PM2.5) and Nitrogen Dioxide (NO2). We estimated the potential health and economic impacts of achieving WHO Interim Target 3 (IT3) and AQG concentrations across LMICs. Methods We conducted a health impact assessment across 136 LMICs to quantify one-year changes in all-cause and cause-specific mortality (chronic obstructive pulmonary disease [COPD], ischaemic heart disease [IHD], and stroke) and disease incidence (COPD, dementia, IHD, and stroke) under WHO IT3 and AQG counterfactual scenarios for PM2.5 and NO2. Concentration-response functions were applied at 1km x 1km resolution. Economic welfare impacts of mortality risk reductions were estimated using country-adjusted values of a statistical life (VSL, Int$ PPP-adjusted 2021). Direct medical and productivity-related costs associated with incident cases were estimated using a cost-of-illness (COI) framework. Uncertainty intervals (UI) reflect uncertainty in concentration-response functions. Results Attainment of WHO IT3 and AQG concentrations for PM2.5 was associated with an estimated 16.04% reduction (6.58million, UI: 6.10-7.07million) and 22.97% reduction (9.43million, UI: 8.75-10.11million) in annual deaths, respectively. Corresponding VSL-based estimates of deaths averted were Int$5.5 trillion (7.0% of aggregate LMIC GDP) and Int$8.4 trillion (10.6% of GDP), respectively. For NO2, IT3 and AQG scenarios were associated with estimated reductions of approximately 1.06% (approximately 435,000 deaths, UI: 388,000-483,000) and 2.79% (435,000 deaths; UI: 388,000-483,000), yielding gains of Int$0.6 trillion (0.7% of GDP) and Int$1.5 trillion (1.9% of GDP). Disease-specific mortality reductions were most prominent for IHD and stroke in Asia and Africa. Under the PM2.5 AQG scenario, an estimated 2.82million (1.67-2.97) COPD, 1.10million (0.83-1.37) dementia, 7.3million (6.41-8.19) IHD, and 2.3million (2.19-2.41) stroke cases could be delayed or averted in one year. Associated reductions in direct medical and productivity-related costs were greatest for IHD, COPD, and stroke. NO2-related morbidity reductions were smaller across all outcomes. All estimates represent one-year changes in risk relative to counterfactual exposure and may reflect delayed rather than permanently avoided events. Discussion Achieving both WHO IT3 and AQG values in LMICs could yield substantial reductions in premature mortality and disease incidence, particularly for cardiovascular and respiratory conditions, alongside large, monetised welfare gains from reduced mortality risk. These findings underscore the considerable societal value of air quality improvements and support accelerated action toward meeting WHO guideline levels in regions bearing the highest pollution burden.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the mechanisms linking EWEs to highly pathogenic avian influenza (HPAI) H5N1 outbreaks remain poorly characterized. The ongoing H5N1 panzootic, responsible for infection in over 500 avian and mammalian species, as well as nearly 1000 human cases and 477 deaths worldwide, provides a critical opportunity to evaluate how climate conditions shape spillover risk at landscape scales. Methods. We compiled a county-month dataset of confirmed H5N1 detections across the contiguous United States from 2022 to 2024 and integrated it with satellite-derived climate metrics, storm event data, and wild bird activity data. We trained and validated a gradient boosting machine classifier to predict outbreak risk and characterize predictor relationships. Results. Our model achieved strong discriminative performance (AUC-ROC = 0.856; AUC-PR = 0.237, representing a 7-fold improvement over chance) and high recall (0.726), supporting its utility as an early warning tool. Human population and temperature-related variables were the most influential predictors: cold temperature shocks and prolonged low temperatures were consistently associated with elevated outbreak risk, likely through enhanced environmental viral persistence, wild bird habitat compression, and allostatic stress-driven immunosuppression in reservoir hosts. Among storm variables, high wind coverage elevated risk, potentially via aerosol dispersal of contaminated particulates, while tornado activity showed an inverse relationship, consistent with documented avoidant behavior in migratory birds. Wild bird reservoir density showed a strong positive monotonic relationship with outbreak risk. Conclusions. Our analyses demonstrate that routinely available environmental and infection data can be used to predict HPAI outbreak risk at fine spatiotemporal scales. These findings demonstrate the divergent roles of short- versus long-term environmental exposures in HPAI spillover dynamics, as well as the potential for machine learning-based surveillance tools to inform targeted biosecurity interventions and early warning systems.

Urbanization in the 21st century has given rise to complex socio-environmental landscapes that contribute to spatial inequalities in health, particularly in the context of emerging infectious diseases, such as dengue fever. This study employs an urban systems framework to explore the multi-dimensional drivers of dengue risk in Ibague, Colombia, where Aedes mosquitoes thrive in diverse urban environments. We characterized the biophysical, socio-economic, and institutional properties of the urban landscape and employed hierarchical cluster analysis to define urban typologies at both census block and urban section levels. Our findings reveal significant differences in dengue incidence across these typologies, with higher rates associated with areas of high population density and commercial activity. Additionally, we examined the landscape configuration and its role in shaping dengue risk, identifying that diversity and intermixing of typologies had protective effects against dengue incidence. This research underscores the importance of considering multi-scale, socio-ecological factors in dengue risk assessments and highlights the need for targeted public health interventions that address the complex interactions within urban landscapes.

BackgroundIn September 2021, the U.S. Centers for Disease Control and Prevention (CDC) implemented the Traveler-based Genomic Surveillance (TGS) program, a surveillance system that leverages genomic sequencing of samples from international air travelers and aviation wastewater for early detection of infectious threats. MethodsDuring September 2021-August 2024, nasal samples were collected anonymously from volunteer international travelers arriving at eight U.S. airports. During February 2023- August 2024, aviation wastewater samples were collected from arriving flights. Nasal samples were pooled and sent to a laboratory for RT-PCR testing. Genomic sequencing was conducted for SARS-CoV-2 and respiratory, gastrointestinal (wastewater), and other pathogens of public health importance. FindingsNasal samples from 694,798 travelers were grouped into 67,308 pools and tested; 13,990 (20.8%) were positive for SARS-CoV-2. Over 80% (400/495) of airplane and 96{middle dot}6% (422/437) triturator (a wastewater collection point from multiple airplanes) samples were positive for SARS-CoV-2. Sequence results were made publicly available a median of 11 days (IQR 10- 13 days) after sample collection. Predominant SARS-CoV-2 variants changed over time. Positive tests for influenza virus and respiratory syncytial virus were high in December/January, and gastrointestinal viruses were detected in wastewater during all months. Monitoring was scaled in response to reported outbreaks of COVID-19 and Mycoplasma pneumoniae in China and clade 1 monkeypox virus in central Africa. InterpretationTraveler nasal and aviation wastewater sampling can provide critical early detection of infectious pathogens before widespread U.S. community transmission. The TGS program provides a model for integrated traveler-based genomic surveillance. FundingCDC Research in ContextO_ST_ABSEvidence before this projectC_ST_ABSWe searched PubMed for relevant studies published during December 1, 2020-August 31, 2024, using the terms "traveler surveillance", "wastewater monitoring", "SARS-CoV-2 genomics", and "airport-based surveillance", without language restrictions. Previous reports have shown the feasibility of using travelers as sentinel populations for disease surveillance. Modeling studies have proposed integrating genomic data into international travel surveillance systems to enhance early pathogen detection, and evidence from Australia, Canada, and the UK suggests such programs could be scalable and effective. Early pandemic-era wastewater surveillance, particularly aviation wastewater, demonstrated that air travel hubs can be used to monitor pathogen importation. Prior efforts largely focused on SARS-CoV-2, with limited integration of multi-pathogen surveillance or side-by-side comparisons of nasal and wastewater surveillance modalities. A limited number of public health reviews have examined the broader implications of airport-based surveillance, including novel methods like airplane wastewater testing. However, empirical data on sustained, large-scale implementation of these models especially outside of regulatory or mandatory testing frameworks have been sparse. Added value of this projectThis is the first real-world implementation and scale-up of an anonymized, multi-pathogen traveler-based surveillance system across multiple U.S. international airports. We developed a scalable framework that integrated nasal swab testing, airplane and airport wastewater sampling, with genomic sequencing into a unified pathogen surveillance platform. Unlike prior efforts which primarily focused on SARS-CoV-2, this program captured respiratory and gastrointestinal viruses simultaneously and tracked genomic variation in near-real time. The program transitioned from a pilot to a multi-modality national surveillance system in under four years, engaging nearly 700,000 international travelers, and nearly 1000 aviation wastewater samples. Our findings demonstrate the feasibility of rapidly adapting this infrastructure for emerging threats and underscores the importance of sentinel surveillance in addressing global sequencing blind spots. Implications of all the available evidenceThe successful scale-up and real-time application of the TGS program illustrates that traveler-based surveillance can serve as a critical global early warning tool. Data generated from this program have filled gaps in global pathogen tracking, informed public health responses to outbreaks, and demonstrated that surveillance of international travelers can be achieved without mandatory testing. The scalability, speed, and adaptability of the program offer a viable model for global replication, especially as routine surveillance capacities decline. Our findings suggest that integration of multi-modal, voluntary traveler surveillance including sequencing and wastewater-based epidemiology should be considered a core component of pandemic preparedness and response frameworks worldwide.

Emerging respiratory disease outbreaks pose a major threat to food production systems. Agricultural workers live in larger, more crowded households than the general population, amplifying their potential exposure to respiratory pathogens, yet the consequences for worker health and food production remain poorly understood. We developed a household-structured susceptible-infectious-recovered (SIR) transmission model to compare disease dynamics between agricultural workers and the general U.S. population across six regions. We simulated outbreaks across a range of epidemiological scenarios and assessed productivity losses in California for three labor-intensive crops (oranges, iceberg lettuce, strawberries) with different harvest seasonalities. For a baseline reproduction number of R0 = 1.5, peak disease prevalence among agricultural workers was 1.23-1.45 times higher than that of the general population across regions, and final outbreak sizes were 1.15-1.28 times higher. Peak productivity losses ranged from 0.50%-0.62% across crops, translating to millions in lost revenue. At higher transmissibility and severity (R0 = 3 and assuming all infections are symptomatic), losses were over 2.5 times higher. Household crowding may lead to disproportionate respiratory disease burden among agricultural workers, highlighting the need for targeted outbreak preparedness and mitigation strategies in the agricultural sector to maintain food system resilience and support public health in these communities.

Background SARS-CoV-2 genomic surveillance data remain limited in most low and middle-income countries (LMICs), resulting in significant gaps in the understanding of variant circulation and evolution. Wastewater-based epidemiology (WBE) presents a non-invasive, cost-effective, and population-representative surveillance approach that can complement clinical testing, particularly in densely populated urban informal settlements with limited healthcare access. This study aimed to pilot wastewater-based genomic surveillance as a multifaceted public health tool in Kenya. Methods A prospective study was conducted using wastewater samples collected from two WHO-validated environmental surveillance sites -- Eastleigh A (Kamukunji sub-county) and Mathare (Starehe sub-county) -- in Nairobi, Kenya, between December 2022 and October 2023. A total of 272 samples were collected using Moore swabs at a frequency of two to three times per week. Samples were concentrated using Nanotrap(R) Magnetic Virus Particles, and nucleic acid was extracted using the Qiagen QIAamp Viral RNA Mini Kit. SARS-CoV-2 was detected using RT-PCR (TaqPath COVID-19 CE-IVD RT-PCR Kit). Library preparation for whole-genome sequencing was performed using the Illumina COVIDSeq kit, and sequencing was conducted on the Illumina MiSeq platform. Bioinformatic analysis was performed using Terra.bio and RStudio, and phylogenetic analysis included sequences abstracted from GISAID. Results Of 272 samples, 238 (87.5%) tested positive with a cycle threshold (Ct) value of less than 36. Genomic analysis of 181 sequences identified Omicron as the predominant circulating variant, detected in 59% of samples. Other variants included XBB (16%), XBB.2.3(10%), XBB.1.9.X (5%), and additional minor variants. These findings were concordant with clinical sequencing data from Kenya over the same period. Conclusions Wastewater-based genomic surveillance reliably reflected SARS-CoV-2 variant trends observed in clinical data. This approach provides early signals of variant emergence and evolution, offering a cost-effective complement to clinical surveillance in resource-limited settings.

Despite significant reductions in malaria cases across Brazil, residual transmission persists in the Legal Amazon, threatening the national goal of elimination by 2035. The Amazonian socio-ecological landscape creates a complex environment where environmental degradation and socioeconomic vulnerabilities intersect. However, the independent and combined effects of these drivers remain poorly quantified at a regional scale. We conducted a retrospective, longitudinal ecological study analyzing a comprehensive panel dataset from 2021 to 2025 across all 773 municipalities in the Brazilian Legal Amazon. We evaluated the independent effects of prior-year deforestation, extreme poverty, population density, fire activity, macroclimatic variables, and primate reservoir abundance on malaria incidence. Deforestation emerged as the dominant predictor of malaria intensity. A one-standard-deviation increase in lagged deforestation area was associated with a 48.3% increase in expected malaria cases. Socioeconomic deprivation also significantly sustained transmission, with extreme poverty increasing cases by 18.8%. Conversely, population density exhibited a strong protective effect, reducing incidence by 72.2%, reflecting the phenomenon of urban protection. While an overall temporal decline of 17.4% annually was observed, profound spatial heterogeneity persisted, with the state of Amazonas maintaining consistently high transmission without a discernible downward trend. Macroclimatic factors and primate abundance did not show statistically significant independent effects at the annual municipal scale. The persistence of malaria in the Brazilian Amazon is not merely a biomedical issue but a profound sustainable development challenge driven by the synergistic effects of land-use change and socioeconomic inequality. Deforestation and extreme poverty create a resilient reservoir of transmission risk that undermines conventional control efforts. Achieving the 2035 elimination goal demands a paradigm shift toward a One Health approach, integrating rigorous environmental protection, targeted social development, and spatially stratified public health interventions. Ultimately, the health of the Amazonian population is inextricably linked to the health of the forest itself.

Fuel availability and fuel dryness are consistently shown to be the primary drivers of wildfire intensity and burnt area. Here we hypothesise that differences in the timing of fuel build up and drying determine the optimal time for wildfire occurrence. We use gross primary production (GPP) as a measure of biomass production and hence fuel availability, and vapour pressure deficit (VPD) as a measure of fuel drying. We use the phase difference in the seasonal time course and magnitude of GPP and VPD to cluster regions that should therefore have distinct wildfire behaviour. We then show that each of the resultant clusters is distinctive in terms of one or more fire properties, specifically number of ignitions, burnt area, size, speed, duration, intensity, and length of the wildfire season. The emergence of distinct regimes as a function of two biophysical drivers reflects the fact that both vegetation and wildfire properties are a consequence of eco-evolutionary adaptions to environmental conditions. We then examine the degree to which human activities or vegetation properties modify these fire regimes within each of these clusters. Variability in GPP and VPD largely explains the within-cluster variation in fire properties. The type of vegetation cover has an influence on burnt area and carbon emissions in particular, while human activities are more important for fire properties such as size, rate of spread and duration largely through their influence of landscape fragmentation. Although both human activities and vegetation properties modify wildfire regimes, the ability to distinguish wildfire regimes using GPP and VPD alone emphasizes that land management, fire use and fire suppression are constrained by environmental conditions. This eco-evolutionary optimality approach to characterising wildfire regimes provides a basis for designing a simple fire model for Earth System modelling.

O_LIAlthough ecological research has long focused on the effects of temperature on population growth, arthropod pests are exposed to a wide variety of environmental factors that affect their performance, such as chemical pesticides targeted against them. Moreover, these environmental factors likely do not act in isolation. Identifying the extent to which abiotic factors interact to affect pest population dynamics can strengthen current and future pest management programs. C_LIO_LIHere, we investigated the extent to which temephos, a common pesticide applied to aquatic environments for mosquito control, influences the thermal performance of juvenile survival and development rate, as well as the intrinsic population growth rate, of the invasive mosquito pest, Aedes aegypti. We implemented a response surface experimental design to measure these traits across seven temperatures and five temephos concentrations and fit temperature- and insecticide-dependent performance curves to assess impacts on the overall performance and the thermal optimum, minimum, and maximum. C_LIO_LITemephos exposure profoundly altered the thermal performance of juvenile survival by reducing survival across all temperatures, shrinking the thermal breadth, and shifting the thermal optimum to warmer temperatures. Through this, temephos also altered the thermal performance of population growth primarily by reducing its thermal breadth. C_LIO_LISynthesis and applications: Our findings demonstrate that interactions between temperature and insecticide exposure can fundamentally reshape pest population dynamics, rather than acting as independent stressors. By quantifying this interaction, we showed that temphos is most effective below the pests thermal optimum, suggesting that larvicides may yield the greatest population suppression in cooler regions or during cooler periods of the year. Incorporating such temperature-dependent efficacy into pest management strategies could improve the timing and spatial targeting of control efforts. More broadly, these results highlight the need to integrate anthropogenic stressors with climatic drivers when predicting pest risk and optimizing management under ongoing environmental change. C_LI

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

1A central question in environmental epidemiology is how the weather affects infectious diseases. Time-series regression (TSR) on population-level case incidence data is widely used to estimate weather effects; however, this design may be biased due to the complexities of infectious disease dynamics, including nonlinear feedback, various types of noise, and latent, dynamic variables such as population immunity. Here, we assess the reliability of incidence-based TSR through a controlled simulation study across four different climates and fifty scenarios representing different pathogens. For each scenario, we simulated 10 years of weekly incidence data using a simple transmission model that included real-world weather data on temperature and relative humidity. We then examined whether the ground-truth weather effects could be recovered from model simulations using negative binomial generalized additive models, a flexible class of TSR models commonly used in empirical applications. We find that these models frequently fail to yield accurate and precise estimates of weather effects, even under favorable conditions such as no process noise and low observation noise (overdispersion). Hence, our results caution against the indiscriminate use of TSR models and suggest that more mechanistic approaches are needed for statistical inference of weather effects from population data.

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.

BackgroundMicroplastics and nanoplastics (MNP) are an increasingly recognized component of airborne particulate matter, yet their impact on respiratory health is unclear. This study aimed to develop a job exposure matrix (JEM) for occupational exposure to airborne MNP (PlastiXJEM(R)) and examine its association with respiratory outcomes in the Lifelines cohort. MethodsFour experts scored occupational airborne MNP exposure levels (none, low, high) for all ISCO-08 occupations based on documented sources and published evidence. After consensus, the PlastiXJEM(R) was applied to baseline current or last-held jobs of 136,928 adult Lifelines participants. Cross-sectional and longitudinal associations with lung function, respiratory symptoms, and asthma were assessed using linear and logistic regression models adjusted for age, sex, smoking, height, BMI, and co-exposure to organic dust, gasses and fumes, pesticides, metals, solvents and silica. ResultsHigh exposure was associated with lower FEV (-43 ml; 95% CI:-61;-25), lower FVC (-47 ml(-69;-26)), lower FEV1%FVC (-0.26 % (-0.51;-0.00) and higher odds of airway obstruction, respiratory symptoms and asthma (e.g. dyspnea OR=1.58; 1.34-1.87). Low exposure was associated with lower FEV1 and FVC in women only. Associations were attenuated after adjustment for socio-economic status but remained for FEV, airway obstruction and dyspnea. MNP exposure was not associated with accelerated lung function decline or with the development of airway obstruction, respiratory symptoms, or asthma. ConclusionOccupational exposure to airborne MNP is associated with lower lung function and a higher prevalence of respiratory symptoms in this cohort. These findings warrant further investigation with complete occupational histories.