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The landscape of emerging zoonoses is being rapidly reshaped by concurrent climate change, environmental transformation, and biodiversity loss. These pressures can alter host populations, pathogen dynamics, and human exposure. Yet, continental-scale evidence linking multi-sector drivers to human infection risk for specific rodent-borne diseases remains limited, particularly for hantavirus. To untangle these influences, we assembled the most high-resolution European hantavirus infection dataset to date (2011–2021), combining large datasets on climatic, environmental, biodiversity, and socio-economic aspects to identify the main drivers of human hantavirus transmission. Using machine learning models, we evaluated the best model for predicting the disease risk in Europe and identified the relative importance of each factor in shaping the risk of human hantavirus infection. Our results showed that maximum temperature in the fourth quarter, Gross Domestic Product (GDP) per capita, and habitat richness emerged as the strongest drivers of human hantavirus disease risk, with non-linear effects varying across regions. Notably, habitat richness, as a proxy for biodiversity, exhibited a strong non-linear relationship with disease risk. Increasing habitat richness was first associated with higher disease risk at intermediate levels, whereas further increases tended to significantly reduce risk. Our results demonstrated that the occurrence of human hantavirus infection in Europe is shaped by multiple cross-sector drivers, highlighting the need to adopt an integrated One Health surveillance approach that incorporates both ecological and socio-economic contexts to improve the prediction of high-risk areas and periods of increased disease transmission. In addition, it is important to emphasize the role of biodiversity in hantavirus infection, particularly habitat richness, as changes in ecosystem diversity can alter the overall risk of disease occurrence. Based on these findings, we propose a mechanistic hypothesis for major regional hantavirus outbreaks, which provides a framework for future research and evidence-based policy development.

Key Features:

• The Climate and Enteric Diseases Research Project features a dataset that contains time series of enteric-disease cases and climate-related exposures, organized by week and at a subnational administrative level.
• Processed enteric-diseases data include deaths, hospital admissions and visits, and surveillance cases with pathogen-specific breakdown from 49 nations with 1290 subnational locations spanning >20 years starting from the year 2000.
• Climate-related exposures include 2-metre temperatures, total precipitation, as well as tropical cyclone maximum sustained winds and total rainfall, which were uniformly extracted and processed by using bespoke R functions, collected from the European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation Land and Inter-Sectoral Impact Model Intercomparison Project 3a datasets.
• Requests for the processed data can be made via https://paulcarlos.quarto.pub/climed/, subject to approval from each data provider.

Background: Tick-borne encephalitis (TBE) is a serious vector-borne neurological disease in Europe, with a complex transmission cycle involving ticks of genus Ixodes, animal hosts, environmental and climatic determinants.

Methods: We modelled annual Geocoded Swedish TBE case data for the period 2005–2023 as a log-Gaussian Cox process in relation to population, environmental and climate data, and wildlife citizen science reports at high spatial resolution. We used the computationally efficient Integrated Nested Laplace Approximation (INLA) and projected the future TBE incidence using fifteen regional climate models.

Results: The covariates significantly associated with TBE incidence in Sweden, ranked based on predictive capacity, were mean temperature, population density, habitat richness, forest cover, precipitation, relative humidity and roe deer density. Specifically, mean temperature above 12° C degrees in the third quarter of the previous year, habitat richness, precipitation in the third quarter, and higher roe deer density were associated with increased TBE risk. The model performed well on testing data, excluded from model building, demonstrating high predictive accuracy in TBE-endemic areas compared to observed data. Our projections indicate TBE cases will increase by 69% under low emissions (RCP2.6) and 121% under high emissions (RCP8.5) by the 2090s, relative to 2014–2023.

Conclusion: The TBE incidence is projected to rise substantially, even under lower emission scenarios. Our findings highlight the growing influence of climate change on TBE transmission in Sweden and provide actionable evidence to inform surveillance, vaccination strategies, and long-term public health planning. Citizen science initiatives and risk maps can help focus resources on areas most vulnerable to transmission. More broadly, the integration of climate models with high-resolution epidemiological data, offers a template for anticipating climate-sensitive vector-borne diseases. Proactive, evidence-based interventions are essential to mitigate the growing health burden posed by TBE in Sweden and beyond.

The relationship between climate change and human hantavirus infection is complex, involving not only climate itself but also environmental conditions, rodent host ecology, and human behavior. Understanding this broader causal pathway requires careful integration of diverse data sources. Compared with well-studied vector-borne diseases, human hantavirus infection remains less explored, partly because its transmission pathways are more complicated to characterize. To assess how current evidence addresses this complexity, particularly the data and methods used, we reviewed empirical studies examining associations between climatic factors and human hantavirus infection in Europe. Thirteen studies were identified through a systematic approach. Using a tailored evaluation framework, we assessed their analytical approaches, including spatial and temporal resolution, lag effects, and handling of modifying and mediating variables. Across studies, we found substantial variation in data and methods. This heterogeneity, combined with differences in scale and variable selection, has led to inconsistent conclusions about whether and how climate influences hantavirus risk across different European regions. Our review highlights the need for more robust, coherent methodological strategies and explicit causal frameworks to clarify the relationships between climate and hantavirus infection and to support future surveillance and public health action.

Non-Technical Summary: This review highlights 10 recent advances in climate change research with high policy relevance, spanning diverse topics: (1) the global temperature jump of 2023–2024; (2) sea surface warming and marine heatwaves; (3) land carbon sinks; (4) interactions between climate change and biodiversity loss; (5) accelerated groundwater decline; (6) global dengue incidence; (7) income and labour productivity loss; (8) strategic considerations for scaling carbon dioxide removal (CDR); (9) integrity of carbon credit markets; and (10) policy mixes for climate change mitigation.

Technical Summary: Interdisciplinary understanding is vital for delivering sound climate policy advice. However, navigating the ever-growing and increasingly diverse scholarly literature on climate change is challenging for any individual researcher. This annual synthesis highlights and explains recent advances across a variety of fields of climate change research. This year, the 10 insights focus on: (1) the record-warmth of 2023/2024 and the elevated Earth energy imbalance; (2) acceleration of ocean warming and intensifying marine heatwaves; (3) northern land carbon sinks under strain; (4) reinforcing feedback between biodiversity loss and climate change; (5) accelerated depletion of groundwater; (6) global dengue incidence; (7) global income losses and labour productivity declines; (8) strategic scaling of CDR; (9) integrity challenges in carbon credit markets and emerging responses; and (10) effective policy mixes for emissions reductions. The insights have been written to be accessible to researchers from different fields, serving as entry-points to specific topics, as well as providing an overview of the evolving landscape of climate change research. In the final section, the insights are used to develop overarching policy-relevant messages. This paper provides the basis for a science-policy report that was shared with all Party delegations ahead of COP30 in Belém, Brazil.

Aedes albopictus, a key vector for Dengue, Chikungunya, Zika, and Yellow Fever, is expanding its range beyond its tropical and subtropical origins, driven by suitable climate, population mobility, trade, and urbanization. Since its introduction to Europe, Ae. albopictus has rapidly spread and triggered recurrent outbreaks. Past model attempts have handled vector suitability and vector introduction as independent drivers. Here we develop a highly predictive spatio-temporal vector diffusion model based on climate suitability and human population predictors, integrated in one simultaneous framework. The model explains how short- and long-range spread of Ae. albopictus interacts with vector suitability, predicting areas of presence or absence with high accuracy (99% and 79%). These results show that the expansion of Ae. albopictus in Europe is predictable and provide a basis for anticipating future outbreaks in situations of dependent interacting co-drivers.

Infectious diseases pose a substantial threat to global health security. Key wildlife species, potentially harbouring numerous zoonotic pathogens, are increasingly being forced to adapt to disturbances from land-use change, human encroachment and climate change. Although the evidence is rather convincing pertaining to the increased risks of zoonotic diseases with degradation and disturbances, the scientific literature on the mitigating effects of ecosystem restoration on zoonotic spillover is scattered, inconclusive and challenged by the lack of a conceptual framework and practical guidance. In light of rising restoration needs and activities, we outline six critical considerations when examining impacts of zoonotic diseases from ecosystem restoration: (1) assessment of zoonotic disease targets; (2) time lag between restoration and recovery; (3) integration of trophic rewilding; (4) robust study designs; (5) controlling for confounding and modifying drivers; and (6) stakeholder engagement and co-creation with communities. Failure to account for these considerations makes the scientific contribution of restoration less valuable and may even jeopardize global efforts to reverse the global biodiversity decline.

This article summarises key contributions from the 27th Nordic Congress of Gerontology (NKG), held in Stockholm in June 2024 under the theme ‘Ageing in a transforming world’. Organised by the Nordic Gerontological Federation, the event brought together over 870 delegates from 44 countries to explore the rapid societal changes affecting older adults. Keynote speakers addressed critical issues such as ageism, the biology of ageing, climate change impacts, social inequalities and international ageing policies. Additionally, the congress offered valuable insights into gerontological education and research funding in Northern Europe. Building on these discussions, this article identifies key areas for advancing ageing research and policy. By amplifying the voices of older people, the congress and the article itself underscore the importance of ensuring that their needs remain central to research, policy and practice. Embracing diverse perspectives on ageing is essential to achieving this goal.

Lyme borreliosis and tick-borne encephalitis significantly impact public health in Europe, transmitted primarily by endemic tick species. The recent introduction of exotic tick species into northern Europe via migratory birds, imported animals, and travelers highlights the urgent need for rapid detection and accurate species identification. To address this, the Swedish Veterinary Agency launched a citizen science initiative, resulting in the submission of over 15,000 tick images spanning seven species. We developed, trained, and evaluated deep learning models incorporating image analysis, object detection, and transfer learning to support automated tick classification. The EfficientNetV2M model achieved a macro recall of 0.60 and a Matthews Correlation Coefficient (MCC) of 0.55 on out-of-distribution, citizen-submitted data. These results demonstrate the feasibility of integrating AI with citizen science for large-scale tick monitoring while also highlighting challenges related to class imbalance, species similarity, and morphological variability. Rather than robust species-level classification, our framework serves as a proof of concept for infrastructure that supports scalable and adaptive tick surveillance. This work lays the groundwork for future AI-driven systems in One Health contexts, extendable to other arthropod vectors and emerging public health threats.