Heatwaves are intensifying as a major climate extreme and have emerged as a growing public health threat in rapidly urbanizing regions such as India. In this study, we integrate long-term heat-related mortality records (1970-2023) with bias-corrected CMIP6 climate projections to quantify future heatwave-related mortality across 67 Indian cities under intermediate (SSP2-4.5) and high-emission (SSP5-8.5) scenarios. A time-series forecasting framework was applied using summer mean temperature as the primary climate driver to project mortality trajectories through the end of the 21st century. Results indicate a strong and sustained increase in heat-related mortality under both scenarios, with multi-fold amplification under SSP5-8.5 relative to SSP2-4.5, reflecting the high sensitivity of health outcomes to emission pathways. Spatial analysis reveals increasing regional divergence under high-emission conditions, with urban regions in the Deccan Plateau, western India, and parts of eastern and northeastern India exhibiting disproportionately higher mortality growth. Multidimensional scaling further highlights emerging clustering of state-level mortality behavior under extreme warming, indicating structurally different regional responses to future heat stress. In contrast, the intermediate mitigation pathway produces more moderate and spatially uniform mortality trends. These findings demonstrate that climate mitigation can substantially reduce both the magnitude and inequality of future urban heat-health burdens. By linking updated climate projections with long-term mortality data at national and sub-national scales, this study provides policy-relevant evidence to support heat adaptation planning and climate-resilient urban development in one of the world's most heat-vulnerable regions.

Survival analysis is widely used for analyzing time-to-event data, and often uncertainty quantification remains challenging in the presence of censoring, limited sample sizes, and heterogeneous populations. Existing Bayesian survival methods provide a framework for incorporating prior information but typically require specification of a full probabilistic likelihood, making inference sensitive to distributional assumptions and sometimes computationally demanding. We propose a Bayesian approach for survival analysis that combines the Bayesian bootstrap with generalized Bayesian (Gibbs) updating. The Bayesian bootstrap first generates a distribution over survival estimators through Dirichlet weights, providing a nonparametric characterization of sampling uncertainty. Prior information for model parameter is then incorporated using a loss function within the generalized Bayesian framework, yielding an inference for posterior distribution. The proposed methodology is model-agnostic and can be applied to a broad class of survival estimators. As an illustration, we develop the framework for the Cox proportional hazards model, producing posterior inference for regression coefficients while preserving the familiar properties and interpretation of hazard ratios. Simulation studies demonstrate that the proposed approach provides robust uncertainty quantification and effectively incorporates prior information. An application to right-censored survival data further illustrates its practical utility. An open-source R package, BayesBoots, implements the proposed methodology.

Introduction Australia faces increasing health pressures from climate-related disasters, particularly bushfires and floods, which interact with existing social and geographic inequities. Traditional health indexes rarely capture these environmental disruptions, limiting their ability to inform climate-resilient health policy. This study develops a holistic, multi-system health-environment index (HEI) tailored to the Australian context, integrating disaster exposure as a core environmental determinant. The aim was to evaluate how disaster-related vulnerability affects sustainable health outcomes across Local Government Areas (LGAs) nationwide. Methods Three domains, health status, socioeconomic conditions, and environmental vulnerability were used to construct the index. The environmental domain was operationalised in three versions based on exposure to all disasters, bushfires, and floods, reflecting Australia’s major climate hazards. Domain indicators were extracted from the 2021 Australian Census via Table-Builder and combined using principal component analysis to produce three separate composite indexes. Welch’s t-tests compared index scores between disaster-exposed and unexposed LGAs. Spatial patterns were assessed using Local Indicators of Spatial Autocorrelation (LISA) to identify clusters of vulnerability. Results LGAs exposed to disasters showed significant lagged effects demonstrated significantly higher (poorer) health-environment index scores compared with unexposed LGAs across all index variations. Spatial analysis revealed clustering, with identifiable hotspots of poor health and environmental vulnerability across Australia. Findings suggest that disaster exposure is a substantial and spatially patterned contributor to poorer population health. Conclusion This study provides the first Australia-wide, multi-system health-environment index that embeds disaster exposure as an indicator of environmental sustainability and climate vulnerability. By mapping hotspots where disaster risk and health disadvantage overlap, the index offers a practical tool for targeted resource allocation, climate-resilient planning, and equitable public health strategies.

INTRODUCTION:Australians living in rural and remote areas experience a higher burden of disease compared to their urban counterparts, whilst having poorer access to essential health services. Socioeconomic status and health workforce shortages are important influences on health status and access to care in these areas. This research aims to provide a local-level analysis of the association between local government area (LGA) indicators of socio-economic status and health workforce availability to enhance understanding of rural and remote workforce distribution patterns. METHODS:Data were extracted from the Australian Bureau of Statistics and the Department of Health and Aged care, which encompassed demographic factors, socioeconomic indicators and counts for medical practitioners, allied health workers and nurses and midwives within non-metropolitan local government areas. Generalised Additive Models with Generalised Estimating Equations (GEE-GAMs) were used to test for an association between socioeconomic status (SES) and the World Health Organisation's definition of health workforce deficit. RESULTS:The odds of being in deficit of nurses and midwives increased with increasing SES. No significant association between SES and medical practitioners or allied healthcare workers was found. Very remote areas were less likely to have a deficit of allied health professionals than inner regional areas, and the same was true for nurses and midwives in both remote and very remote areas. CONCLUSIONS:The findings suggest that health workforce policies that target areas of need based on SES, may have contributed to better availability of nurses and midwives in these locations, but not significantly so for medical practitioners or allied health professionals. Further research is required to investigate the relative success of workforce policies in addressing health need in relation to SES and remoteness.

Arctic sea-ice loss is a defining feature of climate change and offers insight into its impact on mid-latitude air quality. Here, we investigate how variability in Arctic sea-ice extent (ASI) affects ground-level ozone (O_3) across eastern US states through physically and chemically mediated atmospheric pathways. Using observations and causal-inference methods grounded in atmospheric dynamics, we show that ASI drives wintertime ozone variability primarily via indirect meteorological mechanisms, including changes in humidity, temperature, and atmospheric circulation along the polar and subtropical jet streams. Inland regions exhibit the strongest sensitivity, while coastal areas are modulated by marine boundary-layer processes. Seasonal contrasts reveal that Arctic-driven dynamics suppress ozone in winter but can enhance accumulation under certain summer conditions. These findings highlight the importance of Arctic-midlatitude teleconnections in shaping regional air quality and highlight the need to integrate large-scale climate processes into ozone management and climate adaptation strategies.

Community perception of vaccine safety influences vaccine uptake. Our objective was to assess current vaccine safety monitoring by examining factors that may influence the availability of post-vaccination survey data, and thereby the specificity and sensitivity of existing signal detection methods. We used causal directed acyclic graphs (DAGs) and a Bayesian posterior predictive analysis (PPA) signal detection method to understand biological and behavioural factors which may influence signal detection. The DAGs informed the data simulated for scenarios in which these factors were varied. The influence of biological factors such as severity of adverse reactions and behavioural factors such as healthcare-seeking behaviour upon survey participation was found to drive signal detection. Where there was a low prevalence of moderate to severe reactions, false signals were detected when there was a strong influence of reaction severity on both survey participation and seeking medical attention. These findings provide implications for future vaccine safety monitoring.

Abstract Downscaling low-resolution long-range climate forecasts to specific sites, in the absence of historical observations, poses a challenge yet holds significant value for weather-sensitive sectors. Existing methods for statistical downscaling and climatological forecasts, however, necessitate site-specific observations. In this study, we propose a novel solution for generating daily precipitation forecasts at any unmonitored site. We introduce a Bayesian non-stationary truncated spatio-temporal model that uses data from meteorological stations within its surrounding region. The model employs a truncated random process to handle dry days and utilises a Gaussian-process-based spatio-temporal stochastic process to capture non-stationary spatial dependencies. Besides utilising ensemble seasonal forecasts and hindcasts from a climate model, it also incorporates Quantile Mapping (QM) downscaled long-range forecasts for these stations. Our proposed model, Spatio-Temporal Forecast downscaling with QM (STFQM), is employed to downscale climate forecasts from ACCESS-S1 (Australian Community Climate and Earth-System Simulator – Seasonal, version 1) at a 60km spatial resolution to any site within the Murray-Darling basin, Australia. Through comprehensive leave-one-station-out cross-validation, our model demonstrates superior performance in one-month lead time forecasts compared to both the raw ACCESS-S1 forecasts and their QM downscaled counterparts, in terms of accuracy, reliability, and overall ensemble forecast skill. Furthermore, our results illustrate that STFQM is on par with Climatology, a benchmark for seasonal forecasts. Our innovative approach complements existing statistical downscaling techniques, eliminating the need for site-specific observations while providing high-quality localised precipitation forecasts.

The prevalence of low birth weight (LBW) is an important indicator of child health and wellbeing. However, in many countries, decisions regarding care and treatment are often based on mothers’ perceptions of their children’s birth size due to a lack of objective birth weight data. Additionally, birth weight data that is self-reported or recorded often encounters the issue of heaping. This study assesses the concordance between the perceived birth size and the reported or recorded birth weight. We also investigate how the presence of heaped birth weight data affects this concordance, as well as the relationship between concordance and various sociodemographic factors. We examined 4,641 birth records reported in the 2019 Bangladesh Multiple Indicator Cluster Survey. The sensitivity-specificity analysis was performed to assess perceived birth size’s ability to predict LBW, while Cohen’s Kappa statistic assessed reliability. We used the kernel smoothing technique to correct heaping of birth weight data, as well as a multivariable multinomial logistic model to assess factors associated with concordance. Maternally-perceived birth size exhibited a low sensitivity (63.5

Asthma is the most common chronic respiratory illness among children in Australia. While childhood asthma prevalence varies by region, little is known about variations at the small geographic area level. Identifying small geographic area variations in asthma is critical for highlighting hotspots for targeted interventions. This study aimed to investigate small area-level variation, spatial clustering, and sociodemographic risk factors associated with childhood asthma prevalence in Australia. Data on self-reported (by parent/carer) asthma prevalence in children aged 0–14 years at statistical area level 2 (SA2, small geographic area) and selected sociodemographic features were extracted from the national Australian Household and Population Census 2021. A spatial cluster analysis was used to detect hotspots (i.e., areas and their neighbours with higher asthma prevalence than the entire study area average) of asthma prevalence. We also used a spatial Bayesian Poisson model to examine the relationship between sociodemographic features and asthma prevalence. All analyses were performed at the SA2 level. Data were analysed from 4,621,716 children aged 0–14 years from 2,321 SA2s across the whole country. Overall, children’s asthma prevalence was 6.27

Scenario analysis and improved decision-making for wildfires often require a large number of simulations to be run on state-of-the-art modeling systems, which can be both computationally expensive and time-consuming. In this paper, we propose using a Bayesian model for estimating the impacts of wildfires using observations and prior expert information. This approach allows us to benefit from rich datasets of observations and expert knowledge on fire impacts to investigate the influence of different priors to determine the best model. Additionally, we use the values predicted by the model to assess the sensitivity of each input factor, which can help identify conditions contributing to dangerous wildfires and enable fire scenario analysis in a timely manner. Our results demonstrate that using a Bayesian model can significantly reduce the resources and time required by current wildfire modeling systems by up to a factor of two while still providing a close approximation to true results.

Child undernutrition is still a major public health concern in Bangladesh in spite of significant decline in the last few years. Climate change may impact the prevalence of undernutrition and its geographical variability through food security and recurring outbreaks of disease, as well as impede efforts to reduce the undernutrition burden. This study aims to evaluate rainfall and temperature associated with childhood malnutrition in Bangladesh. A spatial mixed effect logistic regression model was used to estimate the association between undernutrition (measured by the composite index of anthropometric failure) and residential area-level rainfall and temperatures, controlling for covariates and spatial effects of residential locations using national data from the 2017 to 2018 Bangladesh Demographic and Health Survey. Our findings indicate that an increase in rainfall was associated with increased odds of undernutrition [adjusted odds ratio (AOR) 1.15, 95% confidence interval (CI) 1.07–1.24], whereas an increase in temperature was associated with decreased odds of undernutrition (AOR 0.86, 95% CI 0.80–0.93). We also found statistically significant structured and unstructured spatial variations in undernutrition, indicating locational effects. Investing in infrastructure and education could be potential strategies for mitigating the negative effects of climate change. High-risk regions in terms of climatic change and malnutrition could be given priority for intervention implementation.

Downscaling daily weather data from multi-week to seasonal forecasts at any location in a region, such as a specific agriculture site without historical weather observations, is challenging but valuable as these localised forecasts can help farmers mitigate weather risks and improve farming production. However, almost all existing statistical downscaling and Climatology methods require site-specific historical observations. To provide site-specific daily precipitation forecasts at any location, this paper develops a Bayesian spatio-temporal non-stationary truncated model for downscaling precipitation from a climate model by leveraging observations from spatially sparse meteorological stations within a region. It uses a truncated random process to model zero precipitation, and a Gaussian-process-based spatio-temporal stochastic process to approximate non-stationary spatial dependency. It makes use of ensemble seasonal forecasts and hindcasts from a climate model, as well as forecasts downscaled by Quantile Mapping (QM) at these weather stations. We employ the proposed model, Spatio-Temporal Forecast downscaling using Quantile Mapping (STFQM), to downscale ACCESS-S1 (Australian Community Climate and Earth-System Simulator – Seasonal, version 1) climate forecasts on the Murray-Darling basin, Australia. Leave-one-out cross-validation results show its superior performance for a one-month lead time in comparison with the ACCESS-S1 raw forecasts and their QM downscaled forecasts in terms of accuracy, reliability and overall ensemble forecast skill. Results also illustrate that the proposed method STFQM is comparable with the forecast skill of Climatology, a benchmark for seasonal forecasts. The proposed method complements existing statistical downscaling techniques, especially without requirements on site-specific observations, to provide quality localised precipitation forecasts.

Quantitative datasets of international conflict skew temporally to modern times and geographically and culturally to the West. Yet post–1815 conflicts featuring Western actors are only a small part of the history of warfare. Many scholars have bemoaned the potential selection bias which this introduces to studies of the causes and effects of military conflict, but as yet quantitative datasets which remedy both these temporal and geographic shortcomings have been lacking. Some datasets have expanded the scope of existing offerings temporally and others spatially, while others have attempted to expand both but with an important lack of detail in terms of location, participants, timing and outcomes. This dataset sets out to remedy the deficit. Using military history’s most extensive encyclopedia of conflict events, we have created a dataset of conflict events spanning the globe and a timescale from 1468BC to the invasion of Iraq in 2003, complete with precise geographic coordinates, year, participants and outcome. We demonstrate the promise of this data-set by using it to assess the frequently asserted relationship between conflict history and economic development, combined with Nordhaus’ GECON sub–national wealth data and historical data on population density from the Netherlands Environmental Agency.

The Standardized Precipitation Index (SPI) is used to indicate the meteorological drought situation - a negative (or positive) value of SPI would imply a dry (or wet) condition in a region over a period. The climate system is an excellent example of a complex system since there is an interplay and inter-relation of several climate variables. It is not always easy to identify the factors that may influence the SPI, or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics that SPI has with the SST, NINO 3.4 and Indian Ocean Dipole (IOD), using a machine learning approach. Our findings are: (i) IOD was negatively correlated to SPI till 2008; (ii) until 2004, SST was negatively correlated with SPI; (iii) from 2005 to 2014, the SST had swung between negative and positive correlations; (iv) since 2014, we observed that the regression coefficient ($\delta$) corresponding to SST has always been positive; (v) the SST has an upward trend, and the positive upward trend of $\delta$ implied that SPI has been positively correlated with SST in recent years; and finally, (vi) the current value of SPI has a significant positive correlation with a past SPI value with a periodicity of about 7.5 years. Examining the complex dynamics, we used a statistical machine learning approach to construct an inferential network of these climate variables, which revealed that SST and NINO 3.4 directly couples with SPI, whereas IOD indirectly couples with SPI through SST and NINO 3.4. The system also indicated that Nino 3.4 has a significant negative effect on SPI. Interestingly, there seems to be a structural change in the complex dynamics of the four climate variables, some time in 2008. Though a simple 12-month moving average of SPI has a negative trend towards drought, the complex dynamics of SPI with other climate variables indicate a wet season for western Australia.

Diabetes is a serious public health issue in developing countries, particularly in urban regions. Heat exposure, measured by residential area land surface temperature (LST), may contribute to the risk of diabetes among urban dwellers due to rapid urbanisation and climate change. This might be useful to predict urban diabetes risk. However, this relationship has not been thoroughly assessed in developing countries. Additionally, residential area greenery may mitigate the detrimental effects of high LST. This study examines the association between residential area LST and diabetes among adults (aged ≥ 18 years) in urban regions of Bangladesh and whether residential area greenness modifies the association. Study data were derived from the latest Bangladesh Demographic and Health Survey 2017–2018, and survey cluster-level LST and enhanced vegetation index (i.e. greenness) were used to define residential area-level environmental features. A binary logistic regression was used to estimate the association, and stratified analysis was performed to examine the effect modification role of greenness. Living in areas with a greater LST increased the odds of having diabetes (AOR 1.23, 95

Urbanization is accelerating in developing countries, which are simultaneously experiencing a rise in the prevalence of overnutrition (i.e., overweight and obesity), specifically among women. Since urbanization is a dynamic process, a continuous measure may better represent it when examining its association with overnutrition. However, most previous research has used a rural–urban dichotomy-based urbanization measure. This study utilized satellite-based night-time light intensity (NTLI) data to measure urbanization and evaluate its association with body weight in reproductive-aged (15–49) women in Bangladesh. Multilevel models estimated the association between residential area NTLI and women’s body mass index (BMI) or overnutrition status using data from the latest Bangladesh Demographic and Health Survey (BDHS 2017–18). Higher area-level NTLI was associated with a higher BMI and increased odds of being overweight and obese in women. Living in areas with moderate NTL intensities was not linked with women’s BMI measures, whereas living in areas with high NTL intensities was associated with a higher BMI or higher odds of being overweight and obese. The predictive nature of NTLI suggests that it could be used to study the relationship between urbanization and overnutrition prevalence in Bangladesh, though more longitudinal research is needed. This research emphasizes the necessity for preventive efforts to offset the expected public health implications of urbanization.

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

Many real-life systems are dynamic, evolving, and intertwined. Examples of such systems displaying 'complexity', can be found in a wide variety of contexts ranging from economics to biology, to the environmental and physical sciences. The study of complex systems involves analysis and interpretation of vast quantities of data, which necessitates the application of many classical and modern tools and techniques from statistics, network science, machine learning, and agent-based modelling. Drawing from the latest research, this self-contained and pedagogical text describes some of the most important and widely used methods, emphasising both empirical and theoretical approaches. More broadly, this book provides an accessible guide to a data-driven toolkit for scientists, engineers, and social scientists who require effective analysis of large quantities of data, whether that be related to social networks, financial markets, economies or other types of complex systems.

Seasonal climate forecasts (SCF) are evolving rapidly alongside improvements in climate modelling and downscaling research, and have great potential for weather-sensitive sectors, especially agriculture, by reducing weather-related risks and increasing productivity. Skilful yield forecasts at the beginning of, or before, a cropping season can provide farmers and other stakeholders in agribusiness with the necessary information for early planning and actions. Only a few yield forecast studies have a forecast lead time of four months or longer due to the problem complexity. To enable SCFs from Global Climate Models (GCMs) to be used for early-season yield forecasts, this paper uses a statistical downscaling technique, Extended Copula Post-Processing (ECPP) and the Schaake shuffle, to downscale four climate variables to generate weather-like daily data that are suitable for agricultural applications. Climate forecasts drive a process-based crop model APSIM (Agricultural Production Systems sIMulator) to simulate crop forecasts on 50 stations, well-distributed across the Australian grain zone. To focus on yield forecast skills attributable to SCF, we propose best practice management rules to predict waterlimited winter wheat yield. Yield forecasts from ECPP have a significant improvement over quantile mapping downscaling and raw SCF from the Australian recent seasonal forecast model ACCESS-S1 in terms of bias, accuracy, reliability, and overall forecast skill. In addition, even at the beginning of a cropping season with a forecast lead time of four or more months, yield forecasts driven by ECPP illustrate higher skill than climatology, a benchmark for yield forecast. Early-season yield forecasts driven by SCFs provides a promising alternative to regression/machine-learning-based forecasts. Performance sensitivity and issues, and gaps on using skilful SCFs to help growers with their farming decision-making are discussed.