Human activities on earth result in global disturbances of natural systems that manifest as natural resource exploitation, pollution, climate change and biodiversity loss which negatively impact human health in turn. In response, the concept of Planetary Health (PH) has emerged, recognizing the need for a systemic understanding of interconnectedness between human health and that of the natural systems on which it depends. Cities constitute a relevant ground for health interventions as they are currently home to more than half of the world’s population and paradoxically also among the most vulnerable locations for the impact of human-induced PH pressures.
Despite its growing scientific prominence, the field of PH lacks in action-based research. Therefore, this review seeks to map the existing evidence of approaches that operationalize the planetary health concept, and its application across urban contexts. It illuminates entry points and provides guidance for PH researchers, educators, local governments and urban planners in the pursuit of operationalizing PH in urban areas.
A literature search for peer-reviewed publications was conducted across 7 databases (N=7843), using the keyword “Planetary Health.” Using the PRISMA-ScR extension guidelines a team of 3 researchers identified 35 articles for the final synthesis.
The included studies consist of various types of research investigating how the concept of PH can be operationalized in urban areas. Some approaches are associated with PHs conceptual foundations in the form of frameworks, literacy/ education models and practices, as well as the formulation of measurement and evaluation methods. Then there are applied approaches, consisting of interdisciplinary PH research-projects, diverse case studies and papers that examine PH’s application potential within policy.
Among the heterogenous application of the concept across a diversity of contexts, the review identified several best practices, draws out present conceptual and research limitations, as well as challenges and opportunities for embedding the concept across diverse disciplines and as part of various urban interventions.
How to cite: Nicke, A. C.: Exploration of Planetary Health Approaches in Urban Areas - A Scoping Review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10279, https://doi.org/10.5194/egusphere-egu26-10279, 2026.
Climate change, land-use intensification, and biodiversity loss are rapidly reshaping animal distributions and human–animal interfaces, altering the geography and seasonality of zoonotic disease hazards. For avian influenza, migratory wild birds act as long-distance carriers while domesticated hosts amplify transmission and generate major socioeconomic impacts through poultry losses, trade disruption, and livelihood shocks. Yet the global, seasonally varying wildlife–livestock interface that underpins spillover and amplification risk remains poorly quantified. Here we develop a data- and model-based indicator that captures the potential for contact between wild avian hosts and key domesticated host groups across seasons. We combine seasonal distribution estimates for thousands of confirmed or putative avian influenza host species with spatially explicit domestic host layers to derive a gridded, season-resolved "interface intensity" index. We then assess whether spatial and seasonal fluctuations in interface intensity align with reported outbreak occurrence. The indicator reveals pronounced seasonal reorganization of high-interface zones, with peak interface intensity concentrated in low latitudes during boreal winter and expanding toward temperate regions in boreal summer. Persistent high-interface areas emerge in parts of Southeast Asia and several regions in Africa, consistent with long-recognized surveillance priorities. Interface intensity is strongly associated with outbreak reports, particularly for poultry in boreal winter, highlighting its value for anticipating periods and places of elevated transmission pressure. Our approach provides a scalable One Health tool that can be integrated with climate and land-use projections to evaluate future shifts in zoonotic risk and to inform targeted surveillance and preventative interventions.
How to cite: Zhang, Q., Li, Z., and Dong, J.: Mapping the seasonal wild bird–livestock interface to support global early warning of avian influenza under planetary change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12421, https://doi.org/10.5194/egusphere-egu26-12421, 2026.
Zoonotic diseases pose significant threats to global health, as evidenced by the COVID-19 pandemic. Despite their impact, our understanding of pathogen spillover mechanisms remains incomplete due to data limitations and methodological challenges. Here we integrate machine learning approaches with ecological models to predict and quantify spillover risks globally. We first systematically assess current limitations in ecological epidemiological modeling, then develop a framework that utilizes pathogen emergence events as critical indicators for spillover risk. Through ensemble machine learning combined with causal inference, we map global spillover risk patterns and identify key climatic, environmental, and socioeconomic drivers. We further apply this framework to tick-borne disease systems across Europe, demonstrating that hierarchical environmental constraints—from macroclimatic phenology to landscape configuration—differentially shape vector abundance and disease prevalence. We show that development intensity sets boundaries for tick population establishment, while landscape features determine realized abundance within climatically suitable areas, with effect magnitudes varying across biogeographic contexts. This interdisciplinary approach advances spillover risk assessment and provides evidence-based guidance for One Health strategies integrating environmental, vector, and human health surveillance.
How to cite: Zhang, Y., Sun, Y., Vanwambeke, S., and Li, S.: An interpretable framework for assessing zoonotic spillover risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6175, https://doi.org/10.5194/egusphere-egu26-6175, 2026.
Groundwater is vital to human and ecosystems, yet it is largely affected by anthropogenic activities, including groundwater extraction and climate change, which have modified groundwater processes and behaviour. This has led to changes in groundwater level long-term trends. Through a collaborative effort involving 47 countries distributed across a range of climatic, geographic, hydrogeological and socioeconomic contexts worldwide, we have collated updated groundwater level data from national monitoring networks. This unprecedented in-situ dataset provides a unique opportunity to conduct a harmonized assessment of groundwater level trends worldwide over the past 20 years. Based on a novel quantitative analysis, we identified regional patterns and hotspots. We conducted a targeted review, linking observed trends to their actual consequences, offering insights into who and what is affected by groundwater changes and how. We show that almost one third of the groundwater levels trends are declining – thus reflecting overexploitation of groundwater – while groundwater levels are rising in 18% of wells – not always indicating a recovery but also the consequence of human impact on the environment. We show that both rising and falling groundwater levels have substantial impacts on water and food security, ecosystems, infrastructure and socioeconomic wellbeing. By linking global groundwater trends with their practical impacts, our work provides the foundation for evaluating whether the adverse impacts of groundwater use and human activities outweigh the benefits, supporting a more effective, evidence-based sustainable groundwater management. It highlights the need for broader international participation and data sharing to ensure continuous refinement of groundwater assessment. Understanding and analysing the impacts at different scales can support decision-making on which impacts are acceptable, which are not, thus supporting the estimation of sustainable groundwater extraction. The extent of the impacts of GWL changes in so many aspects of life underscores the urgent need to integrate and mainstream groundwater in development plans.
How to cite: Lictevout, E., Cao, F., Gerges, E., Ruz Vargas, C., and Pearson, A.: Impacts of groundwater level change on ecosystems and societies worldwide, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19220, https://doi.org/10.5194/egusphere-egu26-19220, 2026.
Increased interactions between humans, animals, and the environment contribute to wildlife habitat fragmentation and increase the risk of infectious disease emergence and transmission. These interactions can be characterized and analyzed through an understanding of land use and land cover (LULC) dynamics and spatial characteristics. LULC characterization is a key preliminary step for addressing eco-epidemiological questions using a landscape-based approach. The landscape, as the observable outcome of the spatio-temporal dynamics of environmental, animal, and human populations and their interactions at different spatial and temporal scales, allows the adoption of One Health and Planetary Health approaches.
Automated analysis and characterization of LULC can be achieved through the application of deep learning techniques to satellite data. However, supervised pixel-level LULC classification using deep learning requires large amounts of expert-verified labeled data. When working with high-resolution imagery, the availability of well-labeled datasets is considerably more limited than for low-resolution products. In addition, class imbalance, underrepresentation of certain land cover categories, and their uneven spatial distribution pose major challenges. As a result, models relying on a single learning task often exhibit limited generalization performance in real-world settings.
To address these challenges, we propose a deep learning autoencoder architecture that leverages both high- and low-resolution land cover maps. The model uses combined optical Sentinel-2 and radar Sentinel-1 data as input to the encoder. During decoding, low-resolution land cover maps are incorporated to capture the global spatial structure of the landscape. This information, introduced at early decoding stages, guides the learning process toward meaningful semantic representations at coarser scales. Subsequently, deeper decoding layers focus on learning finer semantic details under the supervision of high-resolution labels.
We evaluated the proposed approach using the DFC2020 dataset, which consists of 5,128 samples with original LULC maps at 10-meter spatial resolution. Low-resolution supervision maps were generated by downsampling the original labels using nearest-neighbor interpolation. We assessed the impact of introducing deep supervision at different decoder depths. Results show that applying deep supervision early in the decoder with a weighting factor of 0.10 yielded the best performance. The mean Intersection over Union (IoU) improved from 46.28% ± 2.28 to 53.82% ± 0.71 across five independent runs. Moreover, the proposed model outperformed the widely used U-Net architecture, which achieved an IoU of 50.93% ± 1.25.
These results demonstrate the effectiveness of deep supervision in enhancing pixel-level land cover classification by exploiting low-resolution information to improve global feature learning prior to refining fine-scale spatial details. This work was conducted within the framework of the MOSAIC Horizon Europe project, part of the Planetary Health cluster.
How to cite: mobsite, S., Hostache, R., Berti-Équille, L., Roux, E., and Guérin, J.: Improving Land Cover Semantic Segmentation through Deep Supervision, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19324, https://doi.org/10.5194/egusphere-egu26-19324, 2026.
To address climate change and land use and land cover change (LULCc), many studies have introduced new concepts in each field. Notably, One Health and Ecosystem Service (ES) are prominent. Integrating these concepts is essential for a comprehensive evaluation of regional ecosystem health. This study defines the changes in ESs that encompass core One Health pillars (Human-Animal (food)-Environment) as Integrated One Health-Ecosystem Dynamics (IOHED).
To demonstrate this assessment’s applicability, we evaluate the 5-year dynamics (2019–2024) of ecosystem health in Gyeonggi-do, the region the most significant LULCc in South Korea. By analyzing the interrelationships among key ES indicators through the lenses of trade-off, synergy, and degradation. Goals include, 1) quantifying four key ES indicators covering One Health for 2019 and 2024, 2) identifying relationships between services, 3) analyzing the spatial aspects of service degradation, and 4) evaluating the potential of IOHED-based ecosystem health assessments.
To achieve this, the core One Health pillars were matched with the four ES categories: human wellbeing (cultural); crop production (provisioning); biodiversity (supporting); and water supply (regulating). Each ES indicator is evaluated using GeoEPIC, InVEST Annual Water Yield, Habitat Quality (HQ), and Urban Nature Access models. The results of 2019 and 2024 are compared to quantify changes, applying a three-step threshold analysis to distinguish significant signals from noise: 1) a ±5% change rate filter, 2) a 95%, and 3) a 90% confidence interval filter.
We hypothesize that changes in Gyeonggi-do environment between 2019 and 2024 will have changed the balance of IOHED. Given the region’s dynamic land-use shifts, quantifying the four ESs (human well-being, crop production, biodiversity, and water supply) that encompass the core three pillars of One Health through this analysis will reveal that land-use changes to increase crop production in certain areas will lead to degradation of biodiversity and water supply services (degradation) and deepen trade-off between services. In particular, spatial degradation hotspots, which appear mainly in areas where LULCc is severe, will clearly identify the point where existing synergy relationships collapse. The IOHED-based comprehensive health index derived from this case study is expected to provide a key scientific basis for prioritizing sustainable land management and conservation from the perspective of One Health.
This study bridges ES and One Health concepts by demonstrating their practical application in a rapidly changing landscape. The indicators identified and the case-based findings could serve as a methodological cornerstone for future ecosystem health assessments. Furthermore, the study contributes by proposing a statistical approach to integrate and interpret outputs from four disparate models with varying units. However, several limitations remain. First, this study is limited in that it serves as a case study rather than a practical evaluation of the entire country, merely demonstrating its potential. Second, the HQ and UNA models do not sufficiently reflect the unique characteristics of Korea. Therefore, future research should utilize models that incorporate Korea's distinct environmental traits to conduct a nationwide comparison.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(RS-2021-NR060142)
How to cite: Kim, S., Bi, J., and Lee, J.: Integrated Assessment of Ecosystem Services and One Health Dynamics in Gyeonggi-do: A Case study Focusing on Human, Food, and Environment Indicators, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8544, https://doi.org/10.5194/egusphere-egu26-8544, 2026.
Climate change, plastic pollution, and antimicrobial resistance (AMR) represent interlinked global threats that collectively influence the emergence, persistence, and dissemination of antimicrobial-resistant bacteria in aquatic ecosystems. The EU-funded TULIP project addresses these intertwined challenges in rivers, lakes, and coasts as a single, compounded risk to both human and planetary health (https://tulip-project.eu). Within the TULIP project, the Italian case study integrates strategic sampling methods using artificial plastic substrates and combination of advanced molecular and microbiological techniques to investigate the spread of ARBs and the mechanisms driving antimicrobial resistance in aquatic environments. The study is conducted in the Latium Region (Italy) on two urban-influenced surface waters: the Tiber River, classified as a very large river (RL2) under the Water Framework Directive (2000/60/EC), and its major tributary, the Aniene River. Sampling campaigns were conducted from the winter season (November 2024) through the summer season (June 2025) to capture seasonal variability and to assess the influence of temperature fluctuations on the persistence and dissemination of ARBs in aquatic ecosystems. The outcomes of this study are expected to generate robust insights into the key processes underpinning the emergence, and dissemination of AMR in aquatic environments. In particular, the findings are anticipated to provide scientific evidence on the role of plastic waste as an environmental reservoir and transmission vector for antimicrobial-resistant bacteria and resistance genes, highlighting plastics as a potential route of human exposure to AMR via aquatic pathways. Framed within a Planetary Health perspective, this evidence may support the development of nature-based and low-cost mitigation strategies to reduce the environmental release of AMR and associated resistance genes, with particular relevance for regions lacking conventional wastewater treatment infrastructure.
How to cite: Marcheggiani, S., Tchermenscaja, O., Loffredo, M. R., and Ferheen, I.: Linking Plastic Pollution and Antimicrobial Resistance: Insights from the Italian Case Study of the TULIP Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20935, https://doi.org/10.5194/egusphere-egu26-20935, 2026.
Southern Kenya and northern Tanzania form a shared rangeland system where climate stress, land use change, and intensifying human livestock wildlife interactions produce concentrated risks to planetary health. We assess the contribution of One Health Community Clubs in the Amboseli ecosystem of Kenya and the Enduimet Longido landscape of Tanzania, two ecologically connected yet administratively distinct settings. Each club integrates local expertise in environmental monitoring, human health surveillance, and livestock and wildlife health, operationalizing One Health at community and landscape scales.
A spatially explicit approach links community observations to mapped grazing areas, wildlife corridors, settlement growth, and water point networks that shape exposure to disease, ecosystem degradation, and livelihood vulnerability. Long term monitoring from Amboseli, including rainfall, pasture biomass, wildlife movements, livestock health, and human wellbeing, demonstrates how community clubs act as localized observatories connecting environmental diaries with georeferenced datasets. In Enduimet, accelerating fencing, agricultural expansion, and drought driven mobility are tracked through participatory mapping, syndromic disease reporting, and seasonal resource monitoring.
Cross border comparison highlights asymmetric risks within shared ecosystems, particularly around wetlands and dry season refugia. We show that effective scaling depends on networked expansion rooted in spatial units and harmonized indicators, enabling aggregation across landscapes and time to support early warning, adaptive management, and policy relevant planetary health action.
How to cite: Mose, V. and Kimiti, K.: Advancing Planetary Health through One Health Community Clubs in East Africa’s Cross-Border Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3951, https://doi.org/10.5194/egusphere-egu26-3951, 2026.
This paper brings together the reflections of external scientists, academics, and activists collaborating in a community science project, supported by a broader community-based research program, “Supporting our seas through automated and integrated networks (SUSTAIN): strengthening ocean observation and management of risks to coastal ecosystems” in the Philippines. Through this initiative, community development practitioners and scientists from allied fields collaborate with fishers and rural coastal community members in the municipalities of Pagbilao and Puerto Galera. Many communities face increasing vulnerabilities linked to corporate fish cage operations, proliferation of invasive species, pollution, gentrification from tourism, development aggression by energy projects, port construction and expansion, and many others. These issues are often rooted in structures that maintain economic hegemony of urban enclaves over rural communities, eroding rural livelihoods, displacing agricultural and coastal spaces, and widening disparities among populations.
Discussions on Pakikipamuhay (Community Immersion), Pag-organisa ng Pamayanan (Community Organizing), and Kwentuhang Kababaihan (Women's Conversations) offer insights into community science processes and dilemmas, coastal resources and uses, gendered risks, and governance issues in Pagbilao Bay and Puerto Galera. The presentations examine through intersecting lenses how fisherfolk communities collectively analyze and interrogate government and external experts’ marine spatial plans and coastal zoning. This paper hopes to shed light on how community science becomes a tool for emancipatory knowledge production, sharing, and application based on explicit social justice goals and participatory process. In particular, the discussion highlights how communities’ sense-making imagines and creates actions to reject value-free ocean observation research and instead promote a participatory science where coastal communities reclaim their voice and power in coastal resource governance.
Ultimately, this paper aims to glean lessons on community science beyond the implementation of the project, and to think and rethink science work and knowledge co-creation process towards transformative work with coastal communities.
How to cite: Gasmen, H., Salamanca, J. B., Baez, K., Eco, R. N., Fausto, R. M., Savares, C. M., Lagos, D., Tanchuling, Ma. L., Vera, C. A., Vera, M., and Yñiguez, A.: Coastal Currents: Reflections on Community Science towards Participatory Risk Knowledge Building in Coastal Localities of Pagbilao, Quezon and Puerto Galera, Oriental Mindoro, Philippines, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18116, https://doi.org/10.5194/egusphere-egu26-18116, 2026.
Awareness of planetary health, i.e., the understanding of how environmental changes affect human health and wellbeing, is a crucial yet often underestimated prerequisite for the effectiveness of climate change mitigation and adaptation policies. Individuals’ awareness shapes risk perception, supports behavioural change, and public acceptance of environmental and health interventions. This is especially relevant for climate-sensitive health threats, whose emergence and geographic expansion are driven by rising temperatures, altered precipitation patterns, and environmental degradation. Despite their growing relevance, awareness of these indirect and often delayed health impacts of environmental change remains poorly understood.
This study contributes to this challenge by investigating how individual- and territory-level factors jointly shape subjective environmental perceptions, a key dimension of planetary health awareness. Environmental perception encompasses visible and immediate stressors, such as pollution, as well as broader concerns about ecosystem change and associated health risks, including the spread of infectious and vector-borne diseases affecting both human and animal health. These perceptions may influence preparedness, adaptive behaviors, and support for preventive interventions.
We analyze data from the environmental module of PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia), the Italian national health surveillance system, and integrate them with contextual information at the municipal level. Covariates include socio-economic indicators, PM2.5 exposure, and geographical features linked to climate-related risks, including those associated with vector ecology and disease transmission. This integrative framework reflects the inter- and trans-disciplinary nature of planetary health research, combining public health surveillance, environmental epidemiology, and spatial socio-economic analysis. Methodologically, we adopt a penalized semi-parallel cumulative ordinal regression model to address the ordered nature of environmental perception outcomes while allowing for flexible, non-parallel effects of high-dimensional selected covariates. Beyond inference, the model is used as an analytical tool to identify determinants most strongly associated with positive environmental perceptions and with neutrality, the latter interpreted as a potential indicator of limited or uncertain planetary health awareness.
The results reveal substantial heterogeneity across Italian territories, indicating that local environmental and socio-economic contexts play a central role in shaping awareness. Individual characteristics interact with contextual conditions in complex ways, confirming that planetary health awareness emerges from multi-level processes. Greater exposure to hazardous environmental factors, particularly elevated PM2.5 concentrations, is associated with poorer environmental perception, suggesting that respondents can recognize specific environmental stressors that may also serve as proxies for broader climate-related health risks, including vector-borne diseases.
This work demonstrates how combining health surveillance data with contextual environmental information and advanced statistical modeling can enhance the understanding of planetary health awareness. The findings provide policy-relevant insights to support place-sensitive, wellbeing-centered interventions aimed at strengthening public awareness and resilience to climate-driven health threats affecting humans, animals, and ecosystems.
Authors are funded by the European Commission grant 101136652. The five Horizon Europe projects, GO GREEN NEXT, MOSAIC, PLANET4HEALTH, SPRINGS, and TULIP, form the Planetary Health Cluster. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.
How to cite: Stival, M., Andreella, A., Bertarelli, G., Midões, C., Tonellato, S., and Campostrini, S.: Population awareness about the impact of environmental factors on their health: tackling the complexity with appropriate statistical modeling. Examples from the Italian risk factor surveillance system., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6626, https://doi.org/10.5194/egusphere-egu26-6626, 2026.
Lyme disease, caused by Borrelia burgdorferi sensu lato (s.l.), is the most prevalent tick-borne disease in the Northern Hemisphere, posing an escalating global health challenge driven by climate change and land-use transformation. However, mechanistic understanding of how environmental factors govern genospecies-specific transmission remains limited.
We compiled the first comprehensive Eurasian dataset of B. burgdorferi s.l. prevalence, comprising 2,528 records from 522 publications across 43 countries (2000–2023). The dataset encompasses 73 tick species from 6 genera and documents 18 Borrelia genospecies. We applied causal-pathway modeling to disentangle direct, indirect, and cascading effects of climate, land cover, landscape structure, and host biodiversity on pathogen prevalence, with host diversity taxonomically stratified according to genospecies-specific reservoir ecology.
Our results reveal distinct biogeographic patterns shaped by vector-host specificity. Ixodes ricinus dominates transmission in Europe while I. persulcatus prevails in Asia. B. afzelii predominates in Central and Western Europe, whereas B. garinii exhibits transcontinental distribution from Western Europe through Russia to East Asia. Critically, B. afzelii prevalence was co-regulated by climate, forest fragmentation, and landscape diversity, and declined significantly with increasing rodent species richness. This provides the first continental-scale empirical support for the dilution effect hypothesis in Eurasia. Forest fragmentation showed opposing pathways: directly amplifying prevalence through edge effects while indirectly suppressing transmission by enhancing host diversity. In contrast, B. garinii showed no detectable host diversity effects but responded directly to temperature and landscape diversity, reflecting reliance on highly mobile avian hosts whose infection status integrates exposure across multiple migratory stopover sites.
These findings reveal fundamental transmission heterogeneity among genospecies with critical implications for disease surveillance and control. Effective management must integrate genospecies-specific ecology with landscape management, unifying biodiversity conservation, climate adaptation, and planetary health protection.
How to cite: Sun, Y., Zhang, Y., and Li, S.: Host diversity and landscape structure drive genospecies-specific Lyme disease risk across Eurasia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2404, https://doi.org/10.5194/egusphere-egu26-2404, 2026.
Unveiling horizontal gene transfer (HGT) of antibiotic (ARGs) and metal(loid) resistance genes (MRGs) in hospital sewage is critical for surveilling antimicrobial resistance (AMR) mobility that poses huge threats to public health. Using metagenomic shotgun sequencing, we provided an integrate insight into AMR characters and the relevant HGT in untreated sewage from one of the world’s largest comprehensive hospitals from Oct 2022 to Aug 2023. We uncovered higher richness and diversity of ARGs or MRGs than mobile genetic elements (MGEs), while MGEs exhibited the highest abundance, suggesting great HGT potentials. Higher number of ARG, MRG, and MGE subtypes and abundances of putative human pathogens were found in autumn-winter than in spring-summer. ARG- and MGE-carrying prokaryotes outcompeted non-carriers in abundances, and multi-ARG and MGE carriers outcompeted single ones. Resistome supercarriers occupying 25% of prokaryotic abundance harbored higher functional diversity and more metabolic capacity than other prokaryotes, which could be related to more predicted HGT events. Notably, 30%, 22%, and 40% of prokaryote-carrying ARGs, MRGs, and MGEs were associated with HGTs. Diversity variation of plasmids as a critical contributor to HGT was positively correlated with those of prokaryotes and ARGs or MRGs. Plasmids carrying high-risk ARGs (e.g., multidrug and tetracycline types) showed higher abundances than prokaryotes and viruses. Most bacterial taxa may undergo high levels of active viral replication (phylum-specific virus/host abundance ratios > 12). Hundreds of virulent viruses could lyse abundant ARG or MRG supercarriers and hosts of multidrug, multi-metals, and As resistome, whilst one temperate virus infecting multiple Azonexus supercarriers may contribute the HGT of Hg resistome. We found the dominance of stochasticity in assembling of ARGs/MGEs rather than prokaryotes or viruses, which was likely owed to functional redundancy led by HGT. Overall, this study sheds lights on a pivotal role of HGT in driving microbial community and functionality, and provides a guidance for the optimization of the treatment strategies particularly on MGEs.
How to cite: Liu, S.: Close interactions between prokaryotes and plasmids or viruses highlight a pivotal role of horizontal gene transfer in shaping antibiotic/metal(loid) resistome and their prokaryotic supercarriers in untreated hospital sewage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2515, https://doi.org/10.5194/egusphere-egu26-2515, 2026.
We used the cross-correlation wavelet transform analysis to understand connections between the change of climate and climatic variables and the change in the number, appearance, and spread of sand flies and diseases they carry in Portugal. We were particularly interested to understand this dependance to be able to model the numbers and spread of canine leishmaniasis (CanL), a veterinary sand fly borne disease. Efficient prevention of CanL is critically dependent on our understanding of drivers of the disease and effective mechanisms of early warning for veterinary sector. Like other disease vectors, sand flies are vulnerable to climate change and are therefore perfect indicators of how local or even global climatic changes may affect their distribution and the infection incidence and spread of the diseases they transmit.
To understand this dependance, we were using historical datasets from sand fly surveillance from Portugal and diagnostic data from Portuguese veterinary laboratories, as proxy records for the numbers of sick dogs. These two datasets form our animal health record. We cross-correlated it with the corresponding temperature, precipitation, and soil moisture data.
Our results show a pattern of time lags between the changes in hydro-meteorological variables and changes in numbers of sand flies and numbers of CanL cases. We hypothesize that these patterns relate to meteorological conditions during the winter and spring that precedes each sand fly season. We will present and discuss these preliminary results.
Funding: The PLANET4HEALTH consortium is funded by the European Commission grant 101136652. The five Horizon Europe projects, GO GREEN NEXT, MOSAIC, PLANET4HEALTH, SPRINGS, and TULIP, form the Planetary Health Cluster. The CLIMOS consortium is co-funded by the European Commission grant 101057690 and UKRI grants 10038150 and 10039289. The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster.
How to cite: Matic, V., Tošić, M., Xufre, A., Blesić, S., and Maia, C.: Understanding climate drivers of the current and future spread of sand flies and sand fly borne veterinary diseases in Portugal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8214, https://doi.org/10.5194/egusphere-egu26-8214, 2026.
Climate change is reshaping the distribution of vector-borne disease risk in Europe by altering the environmental suitability and phenology of disease vectors such as phlebotomine sand flies, which transmit leishmaniasis. Despite regional observational evidence of sand fly range expansion from Mediterranean areas toward more temperate latitudes, quantitative multi-year diagnostics of such shifts remain limited. Building on the Sand Flies Extreme Prediction Population (FEPO) model, which provides high-resolution daily predictions of sand fly densities across Europe, we introduce a suite of spatio-temporal diagnostics to quantify distributional shifts in density predictions.
We applied these diagnostics to FEPO output for 2021 and 2022 across four Phlebotomus species (P. papatasi, P. perniciosus, P. sergenti, and P. tobbi), using a threshold-based occupancy metric, a density-weighted centroid, and the 95th-percentile front latitude as indicators of spatial redistribution. Using mid-month sampling (one day per month) to balance computational efficiency with seasonal coverage, we detect consistent northward shifts between the two years. Centroid latitude increased by approximately 0.09–0.39° (about 11–44 km) across species, while the 95th-percentile front latitude advanced by approximately 0.17–0.49° (about 19–54 km). The occupied area exceeding a density threshold of 0.1 (model units) increased for all species (0.4–4.5%), with the largest expansion observed for P. perniciosus. Monthly diagnostics further indicate that these shifts are seasonally modulated, with the strongest front differences occurring in the cool season and early spring. As an illustrative example, for P. papatasi, the centroid shifted north by approximately 0.21° (about 23 km) and the front advanced by approximately 0.49° (about 54 km), accompanied by an approximately 2.5% increase in occupied area.
These preliminary two-year diagnostics demonstrate an emergent northward redistribution of predicted sand fly densities in FEPO projections, consistent with broader climatic pressures on vector ecology. While limited in temporal span, the observed shifts highlight the potential of spatio-temporal diagnostics to reveal directional trends in vector population forecasts and to inform public health preparedness.
Acknowledgement: The CLIMOS consortium is co-funded by the European Commission grant 101057690 and UKRI grants 10038150 and 10039289. CLIMOS is one of the six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, forming the Climate Change and Health Cluster. We also thank the EDENext and VectorNet initiatives, as well as the regional data providers and individual contributors, for their essential datasets.
How to cite: Soheili, M., Modiri, E., Rakovec, O., Maia, C., Berriatua, E., Michaelakis, A., Blesic, S., and Samaniego, L.: Spatio-Temporal Diagnostics Reveal Early Signals of Sand Fly Range Shift in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18432, https://doi.org/10.5194/egusphere-egu26-18432, 2026.
African swine fever (ASF) is a transboundary viral disease causing severe impacts on national animal health systems in wild and domestic suids. Since its official report in North Korea in 2019, ASF has posed a persistent threat to livestock production, public health, and ecological safety on the Korean Peninsula. Wild boars are recognized as a key reservoir and vector facilitating long-distance spread of ASF, particularly across national borders. However, in North Korea, critical information on outbreak locations, wild boar population density, and transmission pathways remains inaccessible, making risk assessment and preparedness extremely challenging.
This study aims to estimate the potential origin and spatial and temporal spread of ASF in North Korea, despite severe data limitations, by following the method from Ko and Cho et al. (2023), applying an agent-based modeling (ABM) and machine-learning framework. In the model, we simulate the wild boar migration and ASF virus transmission. Wild boar sounders were represented as agents whose movement, social structure, reproduction, and contact behaviors were parameterized using ecological and physiological information from the literature-based database. ASF transmission was simulated through local contacts among agents in a spatially explicit landscape, and infection trajectories were tracked over time to estimate transmission pathways and the timing of potential arrival at the Demilitarized Zone (DMZ).
Two introduction scenarios were examined based on proximity to reported outbreaks in northeastern China and prior epidemiological evidence: Usi County in Jagang Province, the only officially reported outbreak site in North Korea (Scenario 01), and Hoeryong City in North Hamgyong Province, where suspected early mortality events were reported (Scenario 02). Repeated simulations were conducted for each scenario to identify dominant spread patterns and temporal dynamics.
While Scenario 01 successfully reproduces the large-scale southward diffusion pattern toward the DMZ, and Scenario 02 remains constrained mainly by topography, the model fails to capture the short elapsed time from the emergence of ASF in North Korea to its arrival at the DMZ in 2019. This temporal mismatch indicates that, although wild boar-driven spatial spread is plausibly represented, additional mechanisms—such as human-mediated long-distance transmission, earlier widespread circulation before official reporting, or multiple introductions including trade-related pathways—are likely required to explain the observed dynamics.
Overall, this study demonstrates how agent-based modeling can be used to reconstruct plausible disease spread scenarios in data-scarce regions and provides insights for prioritizing transboundary surveillance and control strategies along the Korean DMZ.
How to cite: Ko, C., Ko, D., and Cho, W.: Estimating the Potential Origin of African Swine Fever on the Korean Peninsula: Backcasting North to South Transmission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9095, https://doi.org/10.5194/egusphere-egu26-9095, 2026.
How to cite: Wanjiku, A., Tran, A., Marti, R., Mose, V. N., and Sosnowski, P.: Integrating Earth Observation and Multi-Agent Modelling to Assess Climate and Land-Use Impacts on Large Herbivore Movement in Amboseli, Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6804, https://doi.org/10.5194/egusphere-egu26-6804, 2026.
MOSAIC is a European project using Open Science to address Planetary Health challenges by co-designing information ecosystems with local stakeholders. One study area is the cross-border rangelands of southern Kenya and northern Tanzania, where Maasai pastoralists face increasing drought, overgrazing, and loss of habitat diversity. In Amboseli National Park, woodland and bushland have declined while grasslands and swamps expanded over the past five decades, affecting wildlife and pastoral livelihoods.
The African Conservation Center combines aerial surveys, plot measurements, and the Normalized Difference Vegetation Index (NDVI) computed by NASA’s MODIS to monitor grazing pressure and total biomass. However, NDVI is least sensitive to living plant biomass during severe droughts, is strongly influenced by soil background, and empirical biomass relationships are difficult to transfer across space and time. The lack of long-term field measurements against which to calibrate remotely-sensed indices remains an essential limitation.
Radiative transfer (RT) modeling simulates the propagation of radiation with all physical mechanisms that lead to remote sensing (RS) acquisitions. It is thus a powerful tool to tackle the latter challenges. Using the DART model, this study aims at quantifying the effect of above-ground biomass (AGB), leaf chlorophyll content, soil type and spatial resolution of RS acquisitions on NDVI values across Amboseli National Park’s 8 main habitats. The methodological objective is to calibrate DART for the Amboseli landscape. Work will focus on compiling instrumental, optical, structural, and geometric parameters through literature review and targeted field measurements. Preliminary results suggest AGB loss and vegetation dryness are processes that can be differentiated by comparing distribution of NDVI values over time, (2) spatial resolution affects the discriminative power of the approach, (3) soil type has a significant influence on the mode of the distribution, even under dense forest canopy.
The final goal is to both develop operational indicators to support local decision-making as well as a transferable and replicable approach, in the spirit of the MOSAIC project.
How to cite: Sosnowski, P., Catry, T., Mose, V., and Mwania, N.: Simulating the radiative transfer budget of the Amboseli National Park (Kenya) to support vegetation monitoring using remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7150, https://doi.org/10.5194/egusphere-egu26-7150, 2026.
Fine particulate matter (PM2.5) is a major environmental health risk, yet long-term, high-resolution exposure assessments remain limited across sub-Saharan Africa. Robust exposure estimates are essential for quantifying health impacts and informing mitigation policies. This study focuses on developing a high-resolution, machine-learning-based PM2.5 dataset for South Africa and demonstrates its application for assessing short-term mortality impacts using country-wide daily respiratory mortality data.
We developed a daily PM2.5 exposure dataset for South Africa using an XGBoost regression framework, trained on ground-based PM2.5 measurements from 2007–2021. Predictors include satellite aerosol optical depth (AOD), meteorological variables (temperature, relative humidity, precipitation, wind speed), soil moisture, road density, population, carbon monoxide (CO), nitrogen dioxide (NO2), emission data from EDGAR, and cyclic temporal predictors (sine and cosine of day-of-year and month). Model performance is strong, with R = 0.95, R² = 0.86, RMSE = 10.9 µg m⁻³, and MAE = 4.15 µg m⁻³, demonstrating high skill in capturing spatial and temporal variability. Using the resulting exposure dataset, we assess population exposure patterns across South Africa and apply a Distributed Lag Non-Linear Model (DLNM) to link district-level daily PM2.5 exposure to all-cause mortality over 1997–2018. Models control for temperature, relative humidity, precipitation, co-pollutants, day of week, and seasonal trends, following established epidemiological approaches. Effect modification by demographic and socio-economic characteristics is explored through stratified analyses.
The high-resolution PM2.5 dataset reveals widespread and persistent exceedances of the South African daily air quality guideline (40 µg m-3). In the highly populated Johannesburg–Pretoria region, PM2.5 exceeds this threshold on more than 50% of days, while elevated concentrations are also common in coastal cities such as Cape Town, Durban, and East London, particularly during winter. Population-weighted PM2.5 exposure has increased by more than 5% nationally between 2000 and 2023, indicating a growing public health concern. Preliminary epidemiological analyses are consistent with existing evidence from comparable settings, suggesting increased mortality risks associated with short-term PM2.5 exposure, with ongoing work to quantify effect sizes and vulnerable sub-populations.
This study provides the first nationwide, high-resolution PM2.5 exposure dataset for South Africa based on machine learning, offering substantial improvements over existing products. The results highlight widespread guideline exceedances, rising population exposure, and the potential for significant health impacts. The framework enables robust future assessments of air pollution - health relationships and supports evidence-based air quality management and health equity policies in South Africa.
How to cite: Chowdhury, S., Kapwata, T., Wright, C., Howlett-Downing, C., Marginean, I., Fossen, E. I. F., and Aunan, K.: High-Resolution PM2.5 Exposure Modelling for Nationwide Assessment of Respiratory Mortality Risks in South Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12838, https://doi.org/10.5194/egusphere-egu26-12838, 2026.
Every city is characterised by a specific climate. Depending on the type of land use, the characteristics of the land cover, such as colour and the permeability of the surface, or the construction materials used in the urban space, there are locally large horizontal and vertical differences in air temperature in the city, defined by the local energy balance of the surface area. The varieties are represented by the topoclimatic units. Each of the topoclimatic types can be characterised by a specific sensitivity to the occurrence of high air temperature, which has its direct impact on the parameters of thermal bioregulation of an individual while in the urban space. The thermal stress impact on health and living comfort is well recognised and defined, but results are presented mostly for big city agglomerations. As European settlement structure is slightly less concentrated, we are willing to examine if medium-sized European cities are sensitive to heatwave stress.
We modelled the response for the conditions of high and extremely high air temperature for four towns in Poland, Central Europe: Wołomin, Pruszków, Wieliczka, Żory, in a resolution of 30 to 30 metres. We demonstrate the relationship between topoclimate and human thermal stress under outdoor conditions of high and extremely high air temperature (30°C and 35°C). The impact of the air temperature on humans is presented as the UTCI index (perceived temperature). Results prove that high, very high and extremely high thermal stress is a significant and important problem in medium-sized cities (40 000-70 000 inhabitants); spatially, thermal stress is strongly related to the density of the urbanised fabric. The most resilient are the topoclimate units containing green and blue infrastructure. Results suggest that targeted actions in urban space – reshaping topoclimates to resilient structures – could play the key role in mitigating the effects of heat waves. These measures are of considerable importance in the context of adaptation to forecast climate change and health protection. Results suggest that high-resolution spatial modelling of human thermal stress could be one of the key parameters in spatial planning as a part of health risk management.
How to cite: Afelt, A., Leziak, K., and Szymalski, W.: City thermal comfort under the heatwave conditions., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22188, https://doi.org/10.5194/egusphere-egu26-22188, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot A
EGU26-20084 | ECS | Posters virtual | VPS31
Beyond Metric-Centric Adaptation: Redefining Occupational Heatwave Governance through Living Lab Co-creationMon, 04 May, 14:51–14:54 (CEST) vPoster spot A
The most critical blind spot in contemporary climate crisis response is the reliance on standardized macro-metrics, which obscure the specific reality of individual suffering. Just as economics uses consumer sentiment to capture household realities and meteorology uses apparent temperature to reflect physiological truths, occupational safety must transition toward integrating perceived risks that exist beyond mere numerical thresholds. This study argues that human perception functions as a high-fidelity biological integration of environmental stressors and conceptualizes it as a Perceptual Trigger: an embodied risk signal with diagnostic and policy relevance. The 2023 fatality of a young logistics worker in Korea illustrates the lethal failure of current systems; while sensors recorded ambient conditions within regulatory thresholds, the system failed to register the worker’s chest tightness—a critical physiological survival signal.
To bridge this gap, a Living Lab for Heatwave Adaptation was implemented in August 2025, engaging 30 port workers from Incheon and 6 technicians from a specialized manufacturer of surface treatment additives as active co-creators. In this study, workers were not treated as mere subjects for data extraction but were empowered as epistemic agents who fundamentally identified and defined hazards within their real-world micro-climates. This study employed the Living Lab methodology as a requisite mechanism to derive Worker Perception Data, which can only be captured within the complex real-world context of the field. Through the systematic qualitative analysis of this co-creation process, the researcher demonstrated that complex heat risks—such as localized radiant heat, engine emissions, and entrapped micro-climates—which are systematically overlooked by standardized sensor arrays, can be effectively rendered into data via worker perception.
The core contribution of this research lies in its translational process: converting Worker Perception Data into systematic risk signals (Information), consolidating them into collectively validated Evidence, and establishing the Policy Grounds for the right to stop work. The researcher proposes a Complementary Governance Model that precisely fills the blind spots of technical sensor monitoring with the acute sensitivity of worker perception data, arguing that this model is a vital mechanism for ensuring site-specific climate adaptation. By framing the datafication of lived experience as an act of Industrial Democracy, this approach serves as an essential interface for connecting grassroots experience with institutional decision-making.
How to cite: Kim, J.: Beyond Metric-Centric Adaptation: Redefining Occupational Heatwave Governance through Living Lab Co-creation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20084, https://doi.org/10.5194/egusphere-egu26-20084, 2026.
EGU26-17834 | ECS | Posters virtual | VPS31
An Early Warning System for sand fly-borne diseases in the Iberian PeninsulaMon, 04 May, 14:54–14:57 (CEST) vPoster spot A
Climate-sensitive vector-borne diseases are increasingly influenced by environmental and climatic variability, posing growing challenges for public health preparedness under climate change. Within the Planet4Health project, an Early Warning System (EWS) is being developed to support anticipatory decision-making for climate-sensitive diseases by integrating climate, environmental, and epidemiological information into operational risk products.
This contribution presents an EWS focused on the sand fly vector (Phlebotomus spp.) and leishmaniasis over the Iberian Peninsula, using machine learning–based modelling approaches. The system integrates high-resolution climate data, climate-derived indicators (e.g. temperature, humidity, and precipitation-related indices), land and environmental variables, and vector presence information to model conditions favourable for sand fly activity and disease transmission. The modelling strategy prioritises interpretable machine learning techniques to ensure transparency and usability for public health and veterinary stakeholders.
The EWS operates across multiple temporal scales, addressing short-term and seasonal forecasts, while also incorporating climate projections to assess potential future changes in environmental suitability for sand flies and associated disease risk. Machine learning models are trained and evaluated using historical climate and entomological data, capturing non-linear relationships between environmental drivers and vector presence while explicitly accounting for uncertainty. Model outputs are translated into spatially explicit risk maps and alert-oriented indicators designed to support operational surveillance and decision-making.
Results from the Iberian sand fly–leishmaniosis case study demonstrate that the EWS successfully reproduces known spatial patterns of vector suitability and seasonal dynamics across the Peninsula, as well as interannual variability linked to climatic anomalies. The modular and data-driven design of the system supports adaptation of the framework to other regions and climate-sensitive diseases, in line with the broader objectives of Planet4Health.
Funding: The PLANET4HEALTH consortium is funded by the European Commission grant 101136652. The five Horizon Europe projects, GO GREEN NEXT, MOSAIC, PLANET4HEALTH, SPRINGS, and TULIP, form the Planetary Health Cluster. The data for EDENext was obtained from the Palebludata website (https://www.palebludata.com). The data for Vectornet was obtained from the ECDC.
How to cite: Natal, S., San-Martín, D., Maia, C., Marme, R., Berriatua, E., Verdú-Serrano, E., Risueño, J., Pérez-Cutillas, P., JImenez, M., and Molina, R.: An Early Warning System for sand fly-borne diseases in the Iberian Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17834, https://doi.org/10.5194/egusphere-egu26-17834, 2026.
EGU26-9608 | Posters virtual | VPS31
Using global climate model simulations for outlooks on how climate change affects future diarrhoea risksMon, 04 May, 14:57–15:00 (CEST) vPoster spot A
Climate models are not designed to provide detailed information on local rainfall that may trigger an outbreak of diarrhoea, but are nevertheless able to reproduce large-scale climatic conditions, processes, and phenomena. Hence, they have a minimum skillful scale, and downscaling makes use of skilfully simulated large-scale aspects in addition to information about how local rainfall depends on those larger scale conditions. The SPRINGS project studies the link between climate change and diarrhoea outbreak through a chain of models, where one stage provides input to the next. It’s important to design such model chains so that they provide a flow of salient and relevant information. This framework also needs to ensure robust results, as different global climate model simulations may give a different regional outlooks. It also needs to involve proper evaluation, and it's important that it is designed for both how the end-results are being used in decision-making, and that the end-results are correctly interpreted in terms of what they really represent. Here, such a framework used in SPRINGS is presented.
How to cite: Benestad, R.: Using global climate model simulations for outlooks on how climate change affects future diarrhoea risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9608, https://doi.org/10.5194/egusphere-egu26-9608, 2026.
EGU26-17758 | ECS | Posters virtual | VPS31
Prioritization of planetary health through health technology assessment: A scoping reviewMon, 04 May, 15:12–15:15 (CEST) vPoster spot A
Background
Policymakers lack structured, evidence-based processes and robust value assessments to guide planetary health investments. Health technology assessment (HTA)—a well-established framework for evidence-informed priority setting—has been proposed to address human and planetary health challenges under climate change. We aimed to assess whether existing evidence on adaptation can inform the prioritisation of planetary health interventions by examining their alignment with HTA criteria and decision-support tools.
Methods
We conducted a scoping review of adaptation interventions targeting climate-sensitive diarrheal disease or its determinants. Nine databases were searched from inception to May, 2025: BIOSIS Citation Index, CINAHL Complete, Econlit, Embase Classic+Embase, Global Health, GreenFILE, Medline ALL, Scopus and Web of Science Core Collection. Data was extracted on climate hazards, adaptation characteristics, outcomes, and HTA-relevant dimensions. Narrative synthesis and evidence gap maps were used to summarise patterns and identify gaps.
Findings
In total, 2924 studies were identified of which 88 studies describing 129 distinct adaptations were analysed. The findings highlight a disparate evidence base, with minimal alignment with HTA evaluative criteria or tools that facilitate prioritization within HTA, such as standardized criteria, economic evaluation and methods for addressing uncertainty.
Interpretation
As climate change alters diarrheal disease patterns, governments must balance investments between current service delivery and future climate risks. Evidence on adaptation for diarrheal disease remains limited to inform such trade-offs from an HTA perspective. These findings highlight research needs for advancing adaptation evaluation and evolving HTA from a human to a planetary health focus.
How to cite: Madriz Montero, A., Kooiman, F., Ruiz, F., Falconer, J., Harris, V., and Bozzani, F.: Prioritization of planetary health through health technology assessment: A scoping review , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17758, https://doi.org/10.5194/egusphere-egu26-17758, 2026.
EGU26-22160 | ECS | Posters virtual | VPS31
The influence of hydrometeorological variables on childhood diarrheal disease: A Planetary Health approachMon, 04 May, 15:18–15:21 (CEST) vPoster spot A
Despite great reductions in the global burden of diarrheal disease, it remains a leading cause of mortality among children under five years old. Climate change threatens these gains, as extreme weather events such as floods, droughts, and heavy rainfall following dry periods are associated with increased risk. The impacts of climate change on childhood diarrheal disease burden depend on interactions between climate hazards, vulnerabilities, and pathogen exposures, although pathogen-specific impacts are not well understood. Improved understanding of how hydrometeorological factors influence pathogen-specific diarrheal disease is needed to predict future diarrheal disease risk and inform preventive action. The SPRINGS project (Supporting Policy Regulations and Interventions to Negate aggravated Global diarrheal disease due to future climate Shocks) brings together scientists from multiple disciplines to collaborate with communities, public authorities, and policymakers to address these challenges within a Planetary Health framework. The case study in Akuse, Ghana integrates epidemiology, environmental sampling, and weather data.
This study aims to determine how hydrometeorological variables influence the incidence of medically-attended diarrheal disease among children under five in Akuse, Ghana. More specifically, this study aims to assess whether the influence of hydrometeorological variables on diarrhea is direct or acts through intermediate impacts on water quality and other water, sanitation and hygiene (WaSH) factors. By identifying climate-sensitive transmission pathways, this study will improve projections of future diarrheal disease risk and identify potential targets for intervention to mitigate the impact of climate change on diarrheal disease in this area in Ghana.
This two-year epidemiological study employs a case-control study design, with a nested case-crossover study. Children under the age of five presenting to four selected health facilities with and without diarrheal disease will be recruited as cases and controls, respectively. Surveys administered by local nurses will collect data about individual- and household-level risk factors, including WASH conditions and animal ownership. In addition, stool samples will be collected to estimate the attributable incidence of diarrheal disease due to four key diarrheal pathogens: rotavirus, Campylobacter, Cryptosporidium, and Giardia. Local weather conditions during the study will be monitored by weather stations positioned near each health facility. Throughout the study, water samples will be collected from various sources in the study area to be tested for multiple water quality parameters, including the presence of the four diarrheal pathogens of interest. Additionally, anthropological research will improve the understanding of human behaviours and perceptions related to diarrheal disease risk and climate change in this area.
By linking weather variability, environmental pathogen presence, WASH factors, and child health outcomes, this study illustrates how a Planetary Health approach can improve understanding of climate-sensitive diarrheal disease risk and provide evidence to inform adaptation strategies and child health interventions in Ghana.
How to cite: Kooiman, F.: The influence of hydrometeorological variables on childhood diarrheal disease: A Planetary Health approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22160, https://doi.org/10.5194/egusphere-egu26-22160, 2026.
