Risk Map Research Articles

Prevalence, spatial distribution and risk mapping of Dirofilaria immitis in wild canids in southern Québec, Canada

Domestic dogs (Canis familiaris) and wild canids, including coyotes (Canis latrans) and red foxes (Vulpes vulpes), serve as definitive hosts for Dirofilaria immitis, a parasitic nematode causing the heartworm disease. Understanding infection risks in wildlife reservoirs in relation to environmental factors is crucial for assessing exposure risk in domestic dogs. The regional prevalence of D. immitis infection was estimated in trapped wild coyotes and red foxes across Québec, Canada. Spatial clusters of infection were detected using Kulldorff's spatial scan statistics. A series of logistic regression models predicting the D. immitis status in coyotes were built from heartworm development unit (HDU) estimates and cumulative precipitation variables over various time periods. Between October 2020 and March 2021, 421 coyotes and 284 red foxes were examined for the presence of D. immitis. The parasite was found in 43 coyotes and 1 red fox. A high-risk infection cluster was detected in coyotes in southwestern Québec. The best model included as sole predictor the average cumulative HDU contributing to risk of D. immitis in the three years preceding coyote capture. This model significantly predicted infection status with an area under the curve of 76.1%. The cumulative precipitation had no notable effect in any model. This study highlights a high prevalence of D. immitis in coyotes in Québec with regional differences correlated to temperature-derived predictors. The spatial risk of infection in this population likely represents the environmental risk of exposure to the parasite given that coyotes do not receive preventive treatment compared to domestic dogs. Our findings are important for veterinarians in the application of prevention strategies for heartworm disease in domestic dogs.

Risk Mitigation Strategies for Sustainable Poultry Supply Chain Management

The livestock sector is an important pillar in providing animal protein and sustaining the rural economy. However, the sector faces major challenges from environmental and socio-economic issues, such as climate change and environmental degradation, which can threaten its sustainability. Negative impacts such as environmental contamination can reduce production quality and quantity and increase supply chain operational costs. This study aims to identify effective risk mitigation strategies to reduce these negative impacts and improve the sustainability of supply chain management. Data were collected from laying duck farms and analyzed using the House of Risk (HOR) method with a Phase 1 and 2 approach. This approach allows the identification of the most critical risks and risk agents and mapping mitigation priorities. Key findings indicate that providing drugs or vaccines to prevent animal virus outbreaks is the highest priority mitigation strategy, while strategic policy decision-making has the lowest priority. Overall, 15 risks and 21 risk agents were identified. This study implies that the implementation of effective mitigation strategies can significantly reduce operational risks, strengthen the resilience of the livestock sector, and support the sustainability of supply chain management as a whole.

Protection of groundwater resources from multi-hazards: An integration of risk assessment and watershed management

The improving groundwater resource protection depends on aligning contamination risk mapping with aquifer protection measures. However, existing methods often fail to align with aquifer protection goals, hindering practical implementation. The study demonstrated integrating vulnerability assessment, hazard evaluation, and groundwater protection initiatives using an L-Matrix approach. Testing this model in a well-documented watershed with multiple hazards to illustrates its applicability, providing a reference for similar scenarios. The risk was assessed through evaluation of aquifer vulnerability, using the DRASTIC method, coupled with the evaluation of a hazard potential considering industrial, livestock/agriculture and urban infrastructure sources in separate. The L-Matrix diagrams consisted of cartesian representations of V versus H, ranging from 0 to 1, segmented into four quadrants bounded by the cut-off lines V = 0.5 and H = 0.5. The quadrants were then linked to aquifer protection measures, with site inspection (high V and H), groundwater monitoring (low V and high H), land use planning (high V and low H), and tolerable expansion of hazardous activities (low V and H). The Paraopeba River basin was used as test site. Mining areas are abundant in this basin and were allocated high values of H and low to high values of V, requesting implementation of groundwater monitoring and periodic inspection of mining sites, even more because groundwater contamination with iron could be clearly associated with the presence of mine-tailings. In the results, it was observed that the DRASTIC vulnerability map of the aquifers has a mean vulnerability around the central value (V = 0.48 ± 0.08). Regarding the distribution of hazards, the average rounds to the value for to (Hindustry = 0.41 ± 0.37, Hagriculture = 0.33 ± 0.09 and Hinfrastructure = 0.27 ± 0.14). Thus, it is noted that for industrial activities, the risk of contamination could be moderate to high, while for agricultural and livestock activities, the risk lies between low and moderate contamination impact.

Pendampingan dan Sosialisasi Protokol Kesehatan Covid-19 sebagai Upaya Persiapan Pembelajaran Tatap Muka

The Covid-19 pandemic has brought a significant impact on the world of education, including in Indonesia. In response, the Indonesian Ministry of Education and Culture issued Guidelines for the Implementation of Learning for the Second Semester of the 2020/2021 Academic Year during the Covid-19 Pandemic on November 20, 2020. SMK Kesehatan Binatama, an accredited "A" educational institution in Sinduadi Village focusing on health sciences, has also felt the impact of this pandemic. A SWOT analysis conducted at SMK Kesehatan Binatama revealed issues related to the school's readiness to meet the health protocol standards set by the government for face-to-face learning. As a solution, the school organized assistance and socialization of health protocols. The socialization and counseling aimed to increase knowledge and awareness about Covid-19, as well as efforts to prevent and reduce the risk of its transmission. The assistance focused on maintaining the consistency of health protocol implementation within the school environment. The steps for implementing community service begin with initial observation, identification of partner problems, application for community service permits, followed by implementation of training activities consisting of health protocol counseling and SOP preparation, and ending with mentoring activities consisting of mentoring risk mapping, compliance with risk implementation and preparation of SOPs. The result of this community service activity is to make Binatama Health Vocational School a role model and pilot project for implementing face-to-face learning in Sinduadi Village. This community service activity has targets and outcomes divided into two aspects: services and products. The service aspect includes improving the school's knowledge and readiness in dealing with Covid-19 and conducting face-to-face learning. The product aspect includes the creation of SOP documents and scientific articles

Mapping the Environmental Risk of Fluoride Exposure of Drinking Water in a Community of Zacatecas, Mexico

Fluoride (iF) is an inorganic element commonly present in groundwater in central Mexico and is considered a health risk when it exceeds the Mexican drinking water standard of 1.5 mg/L. Prolonged exposure to iF can cause various adverse health effects, such as dental fluorosis and neurological effects, particularly in children. A rapid and cost-effective strategy to identify possible areas where the resident population may be at risk of exposure to this contaminant is the health risk estimation methodology of the Pan American Health Organization (PAHO). The aim of this study was to estimate the environmental risk of iF exposure in residents of Jerez, Zacatecas, Mexico, and subsequently construct risk maps to identify areas of the city where there is a higher risk of exposure. Fifty-five tap water samples were collected from households to determine iF concentrations using the ion-selective electrode method. Based on these environmental values, the hazard quotient (HQ) was estimated following the health risk estimation methodology. Subsequently, risk maps were generated from these values to visualize the spatial distribution of high-risk areas within the city. The iF concentrations in tap water ranged from 1.3 to 7.3 mg/L (ppm), with only one sample below the standard of 1.5 mg/L. We estimated HQ values ranging from 0.63 to 3.73. Exposure to iF in tap water is a health problem that must be addressed, necessitating risk communication actions that enable the population to safeguard its health through simple measures, thereby avoiding future health costs.

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

TERRITORY'S INNOVATION ECOSYSTEM: TOOLS FOR MANAGING DEVELOPMENT IN THE DIGITAL ENVIRONMENT

Purpose of the study. The innovation ecosystem of the territory was set to be the format of adaptation of the regional innovation system to the digital environment. This management model is rapidly developing, based on the generalization of empirical experience and multidirectional theoretical research. However, the diversity of management concepts requires building a logical relationship in terms of their application to ensure the successful operation of the ecosystem. Therefore, the purpose of this article is to substantiate management approaches and tools that contribute to improving the performance of the territory's innovation ecosystem in the digital environment. Conclusions. It is proposed to use the model for assessing its maturity as an information basis for managing the development of the territory's innovation ecosystem. A comparative analysis of approaches to the assessment of ecosystem maturity has been carried out. The key aspects of assessment are defined, and the expediency of building a structured dynamic complex of assessment models is substantiated. Promising models of ecosystem management (hybrid orchestration, multi-orchestration, multi-tier orchestration) are identified, which allow for non-hierarchical coordination of relatively independent partners in the process of creating a common value. The landscape of trajectories of possible development of the innovation ecosystem of the territory is constructed, which defines a controlled transition between possible states depending on the ratio of maturity levels of each of the participants and the ecosystem as a whole. The importance of taking risks into account for the possibility of realizing the optimal development trajectory is emphasized. A methodological approach to ecosystem risk assessment is proposed, combining: 1) a map of risks of the territory's innovation ecosystem, structured by life cycle stages and management level (an individual participant and the ecosystem as a whole), 2) risk ranking based on the assessment of their impact on the performance indicators of the territory's innovation ecosystem.

Integrating Remote Sensing and GIS Techniques for Effective Wildfire Risk Mapping and Management

Integrating Remote Sensing and GIS Techniques for Effective Wildfire Risk Mapping and Management

Floodplain Zoning and Mapping for Lower Kelani River, Sri Lanka

Flood risk mapping and floodplain zoning are emerging as practical tools to reduce flood damage. The study intended to develop floodplain zoning maps in the lower Kelani River basin using Flood frequency analysis and MIKE 11 modeling. Flood quantiles estimated for River gauging station at Hanwella and Lower Kelani basin have been used for the flood map developments. The river gauge at Hanwella to the estuary River strip has been considered for floodplain zoning using MIKE 11 modeling software. The hydrologic modeling was performed using MIKE 11 Nedbor Afstromnings Model (NAM), while the hydraulic modeling was performed using MIKE 11 Hydro-Dynamic (HD) model. MIKE 11 rainfall-runoff (RR) NAM calibration and validation Nash-Sutcliffe efficiency (NSE) results were 0.766 and 0.607, respectively. Similarly, coupled NAM and HD model on Mike-Zero platform calibration and validation NSE results were 0.917 and 0.871, respectively. The return period floods estimated to the Hanwella River gauge and Lower Kelani River basin have been availed to generate flood inundation maps. Finally, the flood plain zoning maps have been developed for the Lower Kelani River for 10-, 25-, and 50-year return periods. These maps can be use to limit cultivations and reduce crop losses during monsoons.

Mitigating digital transformation risks for circular construction in emerging economies: an empirical investigation in Vietnam

Although emerging economies are increasingly adopting digital solutions, integrating these technologies with circular construction practices remains relatively unexplored, which could impede the sector’s transformation. This study addresses this gap by investigating the complex risk landscape, identifying 23 critical concerns through a quantitative approach. Utilizing data collected from 248 construction professionals in Vietnam, this study scrutinized and assessed these risks using reliability testing, Kruskal-Wallis test, exploratory factor analysis, relative importance index, and risk mapping techniques. The results revealed the differences in how clients, contractors, and consultants perceived digital transformation risks for circular construction. In addition, the findings indicated the most significant risks, including negative returns on investments, income loss, competition, legal uncertainties, cyberattacks, and high digitization costs. Furthermore, a comparative analysis showed that countries with similar socioeconomic and cultural backgrounds may exhibit markedly different risk profiles. Theoretically, this work bridges circularity and digitalization, offering theoretical insights into risk dynamics in emerging economies. Moreover, the practical implications are substantial, providing valuable strategies for decision-makers and stakeholders in Vietnam and beyond, paving the way for a more resilient and sustainable future for the construction industry.

Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases

Respiratory infectious diseases, such as seasonal influenza, pose significant risks to public health safety through airborne transmission. This study aims to explore methods for controlling airborne infection risk in waiting rooms, a hotspot for cross-infection. A database of 990 samples was established by integrating the results of computational fluid dynamics and social force model simulations into the Wells-Riley equation. We introduced two indicators, Pr (Particle Removal Efficiency) and Hr (High-risk Area Ratio), to evaluate the cleanliness performance of waiting room spaces. Additionally, the Sci (Space Cleanliness Index) was proposed as a criterion for evaluating architectural designs and fine-tuning fresh air system operational parameters. We also unveiled the mapping relationship between spatial morphology parameters and ventilation design parameters in relation to the cleanliness performance of waiting rooms. An infection-risk prediction proxy model was established based on machine learning methods, suitable for rapid prediction of spatial infection risks in waiting rooms and optimization of fresh air system operational parameters. The findings can be applied to the selection of hospital design schemes and the optimization of ventilation operational control strategies, aiding in strengthening infectious disease control and reducing the public health safety risks of cross-infection from respiratory infections during peak seasons in hospitals.

Introducing a climate, demographics, and infrastructure multi-module workflow for projected flood risk mapping in the greater Pamba River Basin, Kerala, India

Enhanced understanding of extreme events is essential for comprehensive risk assessment promoting informed decision-making and early warning to ensure the resilience against challenges imposed by changing climate. This study proposes a novel multi-module integration framework for the comprehensive evaluation of future flood risk in the Greater Pamba River basin, Kerala, India. The integrative modelling framework is developed by assembling a Cellular Automata-Markov Chain (CAMC) based and Use/Land Cover (LULC) projections, Reliability Ensemble Averaging (REA) of Global Climate Model (GCM) rainfall projections, Bayesian approach-based population projection, and spatial analysis of projected infrastructure and Digital Elevation Models (DEMs). By seamlessly interweaving the associated datasets, we illustrate the intricacies of flood vulnerability, exposure, and frequency across multiple return periods (2, 5, 10, and 100, 200 and 500 year) leading to the creation of diagnostic and prognostic high-resolution flood risk maps. The resultant flood analytics and analyses shown that around 36 % of the villages/municipalities and between 70 and 80 % of the critical facilities like schools, hospitals, and flood relief camps are falling under high-risk zones, for the 100-year return period flood. This finding is in line with Kerala's devastating flood episode in 2018, which exhibited characteristics of a nearly 100-year return period flood. In light of a changing climate, population growth, and shifting landscapes, these approaches provide a roadmap for more informed decision-making and adaptive flood risk management measures.

Landslide Risk Assessments through Multicriteria Analysis

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

Developing a flood risk assessment model with genetic algorithm-based weights

To reduce flood risk efficiently within constrained disaster prevention budgets, governments employ economic analyses and qualitative flood risk assessments. However, conventional methods, such as entropy weight and the Analytic Hierarchy Process method, have limitations in terms of the accuracy of the flood risk index. Here, we overcome these limitations by applying a genetic algorithm (GA) – an optimization method mimicking a natural selection process and biological genetic evolution. We developed a new flood risk index by using GA to calculate weights to indicators associated with four items (Hazard, Exposure, Vulnerability, and Capacity) for 161 Korean cities and counties from 2016 to 2021. The indicators (number of buildings, farmland area, dependent population, etc.) for the Exposure and Vulnerability items were reflected in the evaluation only for damaged targets directly exposed to flood risk, using grid cells of indicators overlaid on the flood risk map. Our GA-based method aimed to optimize each indicator’s weights to minimize errors between damage rankings and flood risk index rankings. Results show that our method reduced errors by 21.42 % during 2016–2021, outperforming traditional methods. Therefore, it is easy to identify municipalities that lack disaster prevention capabilities and are vulnerable to flood risk by comparing flood risk indices under the same conditions, such as maximum rainfall index. Our proposed method could better aid local government in decision-making for flood risk mitigation by allocating constrained budgets efficiently.

Multi-contamination groundwater risk assessment based on integrated OSPRC framework considering receptor and consequence components

This study evaluates the risks associated with geogenic and anthropogenic contaminants in the Azarshahr aquifer situated in the Lake Urmia watershed in NW Iran, an area critically affected by both natural mineral deposits and intensive agricultural activities. This region, characterized by its significant geological diversity and extensive use of pesticides and fertilizers, presents a unique opportunity to study the interaction between natural and human-induced groundwater contamination. Employing the Origin-Source-Pathway-Receptors-Consequence (OSPRC) framework, the research focuses on the linkage between these contaminants and waterborne diseases, particularly examining the less-studied “Receptor” and “Consequence” components using the Groundwater Quality Index (GQI) and health risk indices. Our approach integrates vulnerability assessments through the DRASTIC and SPECTR methods, enhanced by Sugeno fuzzy logic, to produce a detailed risk map highlighting two critical zones: Risk Cell 1, impacted predominantly by agricultural contaminants including nitrate and chlorpyrifos, and Risk Cell 2, affected by geogenic contaminants such as arsenic, lead, nickel, and chromium. This comprehensive analysis not only maps out the source and migration pathways of these contaminants but also evaluates their impact on human health. The findings underscore a strong correlation between identified risks and health impacts, emphasizing the pressing need for targeted health interventions and improved management of water resources in the region. By advancing the application of the OSPRC framework, this research fills a vital gap in our understanding of aquifer contamination dynamics and sets a new standard for future groundwater risk assessments.

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Flood Susceptibility and Risk Mapping of Kathmandu Valley Watershed, Nepal

Comprehensive flood risk assessment is often constrained by a lack of appropriate data in high-altitude watersheds, particularly in developing countries like Nepal, where institutional capacities are limited for mapping and monitoring flood-prone communities. This study, one of the first of its kind, produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS). The result shows that most parts of the KV (around 80%) have moderate to low flood susceptibility around the watershed but susceptibility is prominent in southern areas. Highly flood-susceptible regions (about 14%) are found mainly along the riverbanks. Flood vulnerability, primarily influenced by population density and literacy rate, is moderate to low in most areas of the watershed (around 86%), predominantly higher in the central urban areas, and gradually lower towards the edge of the watershed. Flood risks in the major portion of the watershed (around 72%), mainly in the southern and eastern parts, are estimated as moderate to low risk, whereas higher risk zones are found in the central urban areas. The high contrast in flood vulnerability scores across the watershed has mainly contributed to the variation of flood risk zones, as flood susceptibility scores are fairly distributed over the watershed. The study findings will help policymakers develop location-specific sustainable flood risk management strategies for the flood-vulnerable communities in the KV watershed.

Predicting graft survival in paediatric kidney transplant recipients using machine learning.

Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine factors that affect the survival of grafts in paediatric kidney transplantation through the use of machine learning. A retrospective review was conducted on records of paediatric patients who underwent kidney transplantation between 1994 and 2021 and had post-transplant follow-up > 12months. The nearest neighbour method was used to impute missing fields from a total of 48 variables in the dataset. Models including Naive Bayes, logistic regression, support vector machine (SVM), multi-layer perceptron, and XGBoost were trained to predict graft survival. The study used 80% of the patients for training and the remaining 20% for testing. Modelling success was evaluated based on accuracy and F1 score metrics. The study analysed 465 kidney transplant recipients. Of these, 56.7% were male. The mean age at transplantation was 12.08 ± 5.01years. Of the kidney transplants, 73.1% (n = 339) were from living donors, 34.5% (n = 160) were pre-emptive transplants, and 2.2% (n = 10) were second-time transplants. The machine learning model identified several features associated with graft survival, including antibody-mediated rejection (+ 0.7), acute cellular rejection (+ 0.66), eGFR at 3years (+ 0.43), eGFR at 5years (+ 0.34), pre-transplant peritoneal dialysis (+ 0.2), and cadaveric donor (+ 0.2). The successes of the logistic regression and SVM models were similar. The F1 score was 91.9%, and accuracy was 96.5%. Machine learning can be used to identify factors that affect graft survival in kidney transplant recipients. By expanding similar studies, risk maps can be created prior to transplantation.

Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location.

Patient safety reporting and learning system of Catalonia (SNiSP Cat): a health policy initiative to enhance culture, leadership and professional engagement

Patient safety reporting and learning systems (PSRLS) are tools to promote patient safety culture in healthcare organisations (HCO). Many PRSLS are locally developed. WHO Global Action Plan on Patient Safety...