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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.

Despite their critical role as reservoir hosts for many zoonotic diseases, the impact of land-use and land-cover changes (LCLUC) on flying foxes' interactions with humans remains unclear, posing a potential public health risk. To address this, we apply optimal foraging theory and individual-based modelling to simulate flying-fox movement and population dynamics under various LCLUC scenarios. After validating our model against available data, we analyze the effects of agriculturalization, urbanization, forest fragmentation, and reforestation on flying-fox densities across synthetic landscapes of urban, forest, orchard, and water-body habitats. Our findings indicate that habitat disruption—particularly fragmentation through urbanization—significantly increases the risk of zoonotic spillover events by increasing contacts between species. Scenarios of forest degradation reveal that ecologically degraded forest environments can further exacerbate this risk. Additionally, we find that reforestation can alleviate spillover risk. These results underscore the importance of conservation and habitat restoration as critical strategies for mitigating zoonotic disease transmission.

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.

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.

The case fatality ratio (CFR) is a vital metric for assessing the disease severity of novel pathogens. The widely used direct method of CFR estimation—the ratio of total confirmed deaths to total confirmed cases—is inherently simplistic, as it fails to account for the essential time lag between case confirmation to death, and reporting delays. These limitations often lead to biased CFR estimates, particularly in the early stages of outbreaks. This study introduces a novel approach—the distributed-delay method that, like the direct method, utilizes publicly available aggregate time-series data on cases and deaths. It estimates CFR by flexibly incorporating a case-to-death time distribution without requiring a priori assumptions on distribution parameters. Using a fitting approach to forecast case fatalities based on known or assumed case-to-death time distributions, the method consistently recovers true CFR much earlier than the direct method under various simulation settings. These settings reflect variability in disease severity, uncertainties in case-to-death time parameters, and limited knowledge of case-to-death time distributions. It outperforms other methods such as Baud’s, which assumes a non-zero constant case-to-death time, and the Generalized Baud’s method, which allows for a direct comparison with our new approach. While evaluations based on empirical data are challenging, our conclusions are supported by CFR estimates obtained using empirical COVID-19 data from 34 countries. As an added value, this analysis also demonstrates a significant negative association between eventual CFR and the expected case-to-death time within the context of COVID-19 data. Our study highlights the complexities of inferring real-time CFR from aggregate time-series case and death data, highlighting that refining this method can lead to accurate real-time CFR estimations for actual outbreaks.

Over the past century, the Earth’s climate has undergone rapid and unprecedented changes, manifested in a noticeable increase in average global temperature. This has led to shifts in precipitation patterns, increased frequency of extreme weather events (e.g. hurricanes, heatwaves, droughts and floods), alterations in ecosystems, and rising sea levels, impacting both natural environments and human societies, health and wellbeing. Without deep and urgent emission cuts and effective adaptation, the toll of climate change on human health and wellbeing is likely to grow. Here, we address the complex relationship between climate change and health, and discuss ways forward for transdisciplinary research and collaboration that can motivate more ambitious mitigation policies and help develop solutions to adapt to the crisis.

Driven by human-caused greenhouse gas emissions, climate change is increasingly claiming lives and harming people's health worldwide. Mean annual temperatures exceeded 1·5°C above those of pre-industrial times for the first time in 2024. Despite ever more urgent calls to tackle climate change, greenhouse gas emissions rose to record levels that same year. Climate change is increasingly destabilising the planetary systems and environmental conditions on which human life depends.

Authored by 128 multidisciplinary experts worldwide, the 2025 report of the Lancet Countdown on health and climate change is the ninth—and most comprehensive—assessment of the links between climate change and health. The data in this report reveal that, as the health risks and impacts of climate change break concerning new records, progress is being reversed across key areas, further threatening health and survival. However, the evidence in this report also exposes important opportunities to accelerate action and prevent the most catastrophic impacts of climate change.

Temperature influences the transmission of mosquito-borne pathogens with significant implications for disease risk under climate change. Mathematical models of mosquito-borne infections rely on functions that capture mosquito-pathogen interactions in response to temperature to accurately estimate transmission dynamics. For deriving these functions, experimental studies provide valuable data on the temperature sensitivity of mosquito life-history traits and pathogen transmission. However, the scarcity of experimental data and inconsistencies in methodologies for analysing temperature responses across mosquito species, pathogens, and experiments present major challenges. Here, we introduce a new approach to address these challenges. We apply this framework to study the thermal biology of West Nile virus (WNV). We reviewed existing experimental studies, obtaining temperature responses for eight mosquito-pathogen traits across 15 mosquito species. Using these data, we employed Bayesian hierarchical models to estimate temperature response functions for each trait and their variation between species and experiments. We incorporated the resulting functions into mathematical models to estimate the temperature sensitivity of WNV transmission, focusing on six mosquito species of the genus Culex. Our study finds a general optimal transmission temperature around 24°C among Culex species with only small species-specific deviations. We demonstrate that differing mechanistic assumptions underlying published mosquito population models result in temperature optima estimates that differ by up to 3°C. Additionally, we find substantial variability between trait temperature responses across experiments on the same species, possibly indicating significant intra-species variation in trait performance. We identify mosquito biting rate, lifespan, and egg viability as priorities for future experiments, as they strongly influence estimates of temperature limits, optima, and overall uncertainty in transmission suitability. Experimental studies on vector competence traits are also essential, because limited data on these currently require model simplifications. These data would enhance the accuracy of our estimates, critical for anticipating future shifts in WNV risk under climate change