GeoHealth

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.

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

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.

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

The widespread adoption of energy-saving bulbs like light-emitting diodes and compact fluorescent lamps in Zambia has raised significant environmental and public health issues because some of these bulbs contain dangerous materials like mercury. This study sought to evaluate households' understanding and disposal practices of used and damaged energy-saving bulbs in Lusaka, Zambia's Mtendere Compound. A cross-sectional design was used, with structured questionnaires distributed to a randomly chosen sample of households. The research showed that, although most participants were aware of the energy efficiency advantages of these bulbs, they had little understanding of their possible health risks and safe disposal procedures. The majority of households reported throwing away broken and used bulbs with their regular household trash, while only a small percentage followed the suggested disposal procedures. Environmental contamination and heightened health risks are exacerbated by a lack of awareness and inadequate municipal waste management systems for hazardous household waste. The research advocates for improved public education initiatives, the creation of specific collection sites for dangerous waste, and the formulation of explicit national regulations and policies for the handling of discarded and damaged energy-saving bulbs. In rapidly urbanizing areas like Mtendere, tackling these issues is essential for protecting public health and advancing environmental sustainability. Key Words: Knowledge, Practices, Waste Disposal, and Mercury coated bulbs

Background: Beaches are popular summertime destinations in Canada. However, they can be affected by specific fecal pollution sources, increasing the risk of recreational water illness. Objectives: This study was conducted to determine the risks of acute gastrointestinal illness (AGI) among Canadian beachgoers and to evaluate the influence of different fecal indicator bacteria (FIB) and other water quality measures on assessing these risks. Methods: In a prospective cohort design, beachgoers were recruited at sites across Canada from 2023 to 2025. Sociodemographic characteristics and exposures were determined through an on-site survey, with a 7-day follow-up survey to determine risks of AGI. Bayesian mixed-effects logistic regression models were fitted to evaluate the effects of an ordinal water contact variable (no contact, minimal contact, body immersion, and swallowed water) on the incident risk of AGI, with an interaction included for water quality indicators. The levels of six FIB and water quality measures were assessed: Escherichia coli, enterococci DNA, three microbial source tracking DNA markers (human HF183/BacR287, human mitochondria, seagull Gull4), and turbidity. Results: A total of 4085 participants were recruited, with 67.6% completing the follow-up survey. The overall incident risk of AGI was 2.6%. Both swallowing water and body immersion increased AGI risks compared to no water contact: median of 20 excess cases (95% Credible Interval [CrI]: 4, 64) and 5 excess cases (95% CrI: 1, 19) of AGI predicted per 1000 beachgoers, respectively. Escherichia coli and seagull DNA marker levels were associated with AGI among those who had water contact, particularly among those who reported swallowing water. Discussion: While the overall burden of AGI due to beach water contact in Canada was low, increased risks are associated with E. coli levels particularly among those who swallow water. This could be related to fecal contamination from seagulls. However, there is substantial uncertainty in the predicted effect sizes.

Wastewater-based epidemiology provides a scalable, noninvasive framework for population-level infectious disease monitoring, but traditional assays limit detection breadth and genomic insight. To address these constraints, we conducted targeted hybrid capture virome sequencing across 15 Texas cities over three years, from 2023 to 2025, generating [~]3 billion viral reads and identifying more than 900 strains across 374 species. Comprehensive temporal and spatial analysis revealed that the wastewater virome exhibits strong, predictable seasonal patterns, which grouped into three dominant seasonal clusters encompassing human, animal, and plant pathogens. Correlation network analysis revealed numerous positive co-occurrence patterns, including seasonal viral pairings, suggesting that the virome functions as a structured and interconnected ecological system. Leveraging this structure, we developed machine learning models using site-specific historical data to forecast individual viral species one month in advance. Of the 159 species modeled, approximately half achieved prediction performance of Pearsons Correlation Coefficient R{superscript 2} [≥] 0.50, and many exceeded R{superscript 2} [≥] 0.75. Classification models accurately inferred the month and season of sample collection (AUROC > 0.85 and > 0.95, respectively). Predictive features frequently included other viruses and temporal indicators, highlighting networked, seasonal virome dynamics. Sentinel pathogens (e.g., Norovirus, SARS-CoV-2) could be forecast accurately even with limited historical data. Together, these findings demonstrate that the wastewater virome is highly seasonal, interconnected, and forecastable, providing a foundation for proactive, metagenomics-based monitoring and early outbreak detection.

Mass gatherings pose a concern for public health because they are associated with dense crowds, increased social interaction, and travel, all of which can facilitate the rapid transmission of infectious diseases. Wastewater and environmental surveillance (WES) were used for pathogen monitoring during the 2024 NFL Annual Player Selection Meeting (the Draft) in Detroit, MI, an event that drew an estimated 775,000 attendees. Wastewater and environmental samples were queried for respiratory viruses and clinically relevant antimicrobial resistance genes (ARG). WES did not detect an increase in the concentration of monitored respiratory viruses (SARS-CoV-2, IAV, IBV, and RSV) associated with the 2024 NFL Draft. In contrast, WES detected a transient increase in carbapenemase targets in wastewater, primarily driven by a fourfold increase in blaOXA-48. Resistome structure in wastewater was dominated by sampling site characteristics rather than changes associated with the event. The Draft weekend coincided with rainfall-driven combined sewer overflow (CSO), potentially allowing the dissemination of ARG to the environment. In surface waters receiving wastewater effluent, an increase in detection frequency and normalized concentrations for multiple ARG were observed following the Draft. WES provided an overview of pathogen prevalence before, during, and after a large-scale gathering, showing how it can warn of emerging health risks in near real time.

IntroductionIncreasing occurrence of epidemics and pandemics and concurrent emergence of different pathogens calls for multi-sectoral, multi-pathogen preparedness actions. Data on various factors that drive emergence of diverse pathogens can inform evidence-based preparedness by identifying geographies at-risk. When leveraging evidence within a One Health approach, multiple pathogens can be addressed simultaneously, thereby strengthening countries pandemic preparedness efforts. MethodsFor seventeen priority pathogens (avian influenza viruses, zoonotic coronaviruses including COVID-19, hemorrhagic fever viruses including Ebola, Henipaviruses, and arboviruses including yellow fever and Zika), we identified global evidence on animal reservoirs, vectors, environmental suitability, and reported human cases. We discriminated geospatially recorded pathogen detections from a background sample and constructed maps using these datasets to generate an evidence-based assessment of emergence risk globally. ResultsSeventeen pathogen-specific assessments were combined into a global composite map. Sub-Saharan Africa and South Asia have evidence supporting emergence risk for the greatest number of pathogens (included areas at-risk of all pathogens) and scored highest when strength-of-evidence weightings were factored. The Americas had the lowest tally of considered pathogens. Environmental suitability analyses received the highest weights, reservoir ranges the lowest. DiscussionPreparedness and readiness must consider the range of global biological threats. Our methodology is capable of incorporating changing evidence on emergence potential for multiple pathogens to identify geographies at higher risk with different pathogen combinations. Our maps can contribute to existing decision-support structures, guiding shared interventions and strategic allocation of resources for spillover prevention and pandemic preparedness, thereby enhancing local response capacities applying a multidisciplinary approach. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSUsing PubMed, we searched for "[PATHOGEN] Preparedness Map" for each of seventeen priority pathogens to explore what resources might exist to be used to guide contemporary preparedness actions. The seventeen pathogens were: avian influenza viruses (AIV,all subtypes), chikungunya virus (CHIKV), Crimean-Congo hemorrhagic fever virus (CCHF), dengue virus (DENV), Ebola virus (EBV), Hendra virus, non-specific Henipaviruses, Lassa virus (LASV), Marburg virus (MARV), Middle East respiratory syndrome coronavirus (MERS-CoV), monkeypox virus (MPXV, all clades), Nipah virus, Yersinia pestis, Rift Valley fever virus (RVF), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yellow fever virus (YFV), and Zika virus (ZIKV). We also searched for "Emergence Preparedness Map" to try and identify a singular resource that housed all these pathogens. Searching for specific pathogens identified resources that had deployed specific approaches or types of data in answering this question, but often did not collate multiple varied evidence streams. Similarly, the more detailed resources tended to be more geographically restricted in scope. When searching for emergence resources more broadly, we identified some clusters of epidemiologically related pathogens being synthesized (for instance thinking about integrated management of vector-borne diseases), but none that spanned the full repertoire of pathogens listed. Others attempted to characterize the phenomena of emergence more broadly, but as a result lost the ability to further capitalize on pathogen-specific activities since pathogens were not a building block within a broader methodology. Added value of this studyIn evaluating the emergence potential for seventeen priority pathogens, we have collated the widest range of pathogens into a common map for synthesis. In doing so, we provide a support mechanism for actionable next steps for epidemic and pandemic preparedness at scale that leverage current knowledge. Contrasting to prior assessments, we leverage different types of data and provide a mechanism to differentially weight their inclusion. We outline a mechanism by which even for pathogens where comprehensive or detailed data is not present, the information currently available can be acknowledged and integrated, to provide immediate support for decision-making, while future enhancements are integrated when available and iterated upon. We also demonstrate how this modular methodology allows customized aggregations of pathogens where scopes of work necessitate - for instance, collating all pathogens with similar vectors where vector control actions can be undertaken. We show with examples of Marburg virus disease in Equatorial Guinea, how the maps demonstrated the prior evidence-base related to emergence of this disease in that geography and use that example to outline how these maps can indicate geographies of concern. Implications of all the available evidenceEpidemic and pandemic preparedness is multi-faceted and multi-sectoral; some actions require pathogen specific insights, while other actions will work to counter a group of pathogens simultaneously. With this methodology, we demonstrate that it is possible to integrate data from diverse formats across different transmission routes and pathogens ecological dynamics globally to produce a set of resources to support local, regional, and global evidence-based decision making. Different groupings can be called upon to support different actions - pathogen specific maps where pathogen-specific vaccination schedules need to be undertaken; tracking the full pathogen-set that any given reservoir is implicated in; determining the differential diagnosis needs for a specific health facility and corresponding population it serves as a function of the implicated local pathogens or their potential future emergence; and supporting local health facilities in developing protocols, training, and necessary equipment to effectively detect and respond to possible local cases. Finally, these maps are designed to evolve alongside advancing infectious disease intelligence, allowing for continuous enhancement and resolution of data limitations across diverse surveillance systems and national contexts.

Scrub typhus remains a persistent public health concern with strong spatial and temporal variability. This study analyses the spatio-temporal distribution, clustering patterns, and forecasting of scrub typhus across five districts, Chittoor, Ranipet, Tirupattur, Vellore, and Tiruvannamalai, using long-term surveillance data from May 2005 to May 2024. We applied spatio-temporal exploratory analysis to identify trends, seasonal behaviour, and inter-district heterogeneity in disease incidence. Hotspot analysis was conducted using the Getis-Ord Gi* statistics to detect statistically significant hotspots and coldspot clusters and examine their evolution over time. To support decision-making, we developed statistical, machine learning (ML), and deep learning (DL) based forecasting models using monthly scrub typhus and climatic features. Root mean square error (RMSE), and R-square error (R2) evaluation metrics are used to compare the performance of the prediction model. Scrub typhus shows clear and recurring seasonal peaks across all five districts, and incidence increases are associated with precipitation, dew point, relative humidity, and vegetation cover. Temperature shows a strong negative correlation, while relative humidity and normalized difference vegetation index (NDVI) show strong positive correlations in all districts. Hotspot analysis identifies Vellore and Chittoor as persistent core transmission zones, with weaker clustering in surrounding districts. Forecasting results indicate that model performance varies by location. The results reveal persistent hotspots, clear seasonal signals, and short-term forecasts across districts. This integrated spatiotemporal and forecasting framework provides actionable insights for targeted surveillance and timely intervention strategies to control scrub typhus.

Wastewater surveillance (WWS) is established as a vital tool for monitoring polio and SARS-CoV-2 with potential to improve surveillance for many other infectious diseases. This study evaluated the feasibility of detecting measles virus (MeV) RNA in wastewater as part of a national WS preparedness trial in Brisbane, Australia, from March to June 2025. Composite and passive sampling methods were employed in parallel at three wastewater treatment plants serving populations between 230,000 and 584,000. Nucleic acids were extracted and analyzed using RT-qPCR targeting MeV N and M genes to distinguish wild-type and vaccine strains. MeV RNA were detected in both 24-hour composite and passive samples on May 26 to 27, 2025 from the largest catchment of 584,000 which also included an international airport. No measles cases were reported in this city or region within 4 weeks of the WS detections. These were confirmed as vaccine-derived measles virus (MeVV) strain via specific RT-qPCR assay. Extraction recoveries varied (11.5% to 70.5%), with passive sampling showing higher efficiency. This is the first report of use of passive samples for detection of MeV. These findings are consistent with other studies reporting WWS results of both MeVV genotype A and wild type genotype B and/or D. It demonstrates the potential for sensitive MeV WWS with rapid differentiation of MeVV from wild type MeV shedding, including in airport transport hubs and with different sample types. Use of WWS could strengthen measles surveillance by enabling rapid detection of MeV RNA and supporting outbreak preparedness and response. This requires optimised methods which are specific to or differentiate wild-type MeV from MeVV. Furthermore, the successful detection of MeV using passive sampling in this study highlights its potential for deployment in diverse global contexts which may include non-sewered settings.

Justification: Accidental lab-acquired infections (LAIs) with potential pandemic pathogens (PPPs) in high-biosafety research facilities risk causing a pandemic. Routine testing of lab workers for LAIs coupled with isolation of infected workers could reduce the risk, but the impact of such an intervention may depend on pathogens' epidemiological characteristics. Objective: This study aims to understand how the epidemiological characteristics of PPPs moderate the efficacy of a routine testing and isolation intervention in preventing larger outbreaks after an LAI. Methods: We employed a discrete-time stochastic network infectious disease model to run 625,000 epidemic simulations encompassing 625 unique combinations of five parameters of interest: test frequency, pathogen transmissibility, the self-isolation rate for symptomatic cases, the percentage of cases that are asymptomatic, and the percentage of infectious time that is spent in the pre-symptomatic state among those who show symptoms. To summarize the Monte Carlo simulations, we paired visual analysis with logistic regression for formal hypothesis testing, with an emphasis on the interaction terms that capture the moderating effect of epidemiological parameters on the impact of test frequency. Main Results: There were four main findings. First, the relative reductions in risk of outbreak that were caused by increased test frequency were inversely correlated with pathogen transmissibility. Second, the effect of test frequency was magnified at higher asymptomatic shares when the symptomatic self-isolation rate was high, but minimally when the self-isolation rate is low. Third, the direction of how the symptomatic self-isolation rate moderated the effect of increased test frequency depended on the asymptomatic share. Fourth, as the pre-symptomatic share of infectious time increased, the effect of test frequency on the probability of an outbreak was strongly magnified largely independent of symptomatic self-isolation rates. Conclusions: Routine testing and isolation could significantly mitigate the risk of catastrophic PPP escapes, with the intervention's success varying based on pathogen characteristics. High shares of asymptomatic and pre-symptomatic transmission notably increased the relative risk reductions achieved by the intervention. These findings suggest prioritizing testing interventions for pathogens with high asymptomatic and pre-symptomatic transmission and highlight the symptomatic self-isolation rate as a policy intervention target.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.