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Background: Borrelia miyamotoi and Borrelia burgdorferi sensu lato (s.l.) are important zoonotic agents transmitted by Ixodes ricinus ticks, which are widely distributed across Central Europe. Understanding the spatial distribution of these pathogens' prevalence will help identify areas with increased infection risk and facilitate the implementation of effective preventive measures. Methods: We analysed 12,955 I. ricinus ticks collected from 142 towns in the Czech Republic between 2016 and 2018. The ticks were pooled into 2591 groups of five and tested using duplex quantitative polymerase chain reaction (qPCR) for the presence of B. burgdorferi s.l. and B. miyamotoi. For each location, we estimated the overall prevalence of both agents using the EpiTools Epidemiological Calculator for pooled samples and calculated the minimum infection rate (MIR). To assess the potential risk of infection, we combined data on the abundance of nymphs and females with pathogen prevalence at each sampled site. Using a geographic information system (GIS), we mapped the MIR and infection risk of both Borrelia species across all 142 sampled locations and employed a geostatistical method (ordinary kriging) to predict MIR values and infection risk as continuous surfaces across the entire country. Results: We detected B. miyamotoi in 110 localities and B. burgdorferi s.l. in all 142 localities. The estimated prevalence of B. miyamotoi and B. burgdorferi s.l. in the collected ticks was 2.1% (95% confidence interval [CI] 1.8–2.3) and 27.1% (95% CI 26.0–28.3), respectively. For B. miyamotoi, we identified previously unknown, geographically distinct hotspots of MIR up to 8.3%, with MIR slightly higher in females (2.3%) than in males (1.9%) and nymphs (1.8%), though the difference was not statistically significant. In contrast, B. burgdorferi s.l. exhibited ubiquitous presence, with consistently high prevalence nationwide, showing similar MIRs in females (16.2%) and males (16.1%), and slightly lower in nymphs (15.6%). The highest infection risk for B. miyamotoi was 12.4 infected vectors per hour in southeastern Moravia, while the highest risk for B. burgdorferi s.l. reached 78.6 infected vectors per hour in the Bohemian-Moravian Highlands. Conclusions: Borrelia miyamotoi is widespread, forming distinct high-prevalence areas in certain regions. Borrelia burgdorferi s.l. demonstrates consistently high prevalence across most of the country, except for a few localized areas such as southwestern Czechia. Both pathogens exhibit natural nidality, forming regions with elevated prevalence and infection risk. Long-term time-series data are needed to confirm the spatio-temporal stability of these hotspots. [ABSTRACT FROM AUTHOR]

Abstract Background Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. Methods This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. Results A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. Conclusions While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock.We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds.Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal.Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health. [ABSTRACT FROM AUTHOR]

Background: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. Methods: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. Results: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. Conclusions: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals. [ABSTRACT FROM AUTHOR]

Rising temperatures are predicted to expand the range of Aedes aegypti and Aedes albopictus, potentially leading to new areas vulnerable to dengue outbreaks. The recent arrival of Aedes aegypti in Upper Egypt underscores this emerging threat, with documented outbreaks in the Red Sea Governorate (2017) serving as evidence. To address this, a cross-sectional surveillance study was conducted during the summer of 2023 in Hurghada City, Egypt, to identify established dengue vectors and their breeding sites. The presence of Aedes aegypti was confirmed, and entomological indices were calculated to assess vector distribution. Risk maps were generated to evaluate potential risks across the city. The study confirms the establishment of Aedes aegypti in Hurghada and provides valuable data for assessing future vector distribution and potential dengue outbreaks. These findings contribute to understanding how global warming and socioeconomic factors influence dengue transmission and will aid in developing effective control measures to prevent future outbreaks in Hurghada. [ABSTRACT FROM AUTHOR]

Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g. Zaragoza, Logroño), mainland areas (e.g. Madrid, Toledo) and in the South (e.g. Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.

Epizootic hemorrhagic disease (EHD) is a non-contagious arthropod-transmitted viral disease and a World Organization for Animal Health (WOAH)-listed disease of domestic and wild ruminants since 2008. EHDV is transmitted among susceptible animals by a few species of midges of genus Culicoides. During the fall of 2021, a large outbreak caused by the epizootic hemorrhagic disease virus (EHDV), identified as serotype 8, was reported in Tunisian dairy and beef farms with Bluetongue virus (BTV)-like clinical signs. The disease was detected later in the south of Italy, in Spain, in Portugal and, more recently, in France, where it caused severe infections in cattle. This was the first evidence of EHDV-8 circulation outside Australia since 1982. In this study, we analyzed the epidemiological situation of the 2021–2022 EHDV outbreaks reported in Tunisia, providing a detailed description of the spatiotemporal evolution of the disease. We attempted to identify the eco-climatic factors associated with infected areas using generalized linear models (GLMs). Our results demonstrated that environmental factors mostly associated with the presence of C. imicola, such as digital elevation model (DEM), slope, normalized difference vegetation index (NDVI), and night-time land surface temperature (NLST)) were by far the most explanatory variables for EHD repartition cases in Tunisia that may have consequences in neighboring countries, both in Africa and Europe through the spread of infected vectors. The risk maps elaborated could be useful for disease control and prevention strategies. [ABSTRACT FROM AUTHOR]

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L'Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune "a-posteriori" vulnerability score, deduced from the observed damage, which is compared to an "a-priori" vulnerability one, evaluated taking into account characteristic indexes for building's typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed. [ABSTRACT FROM AUTHOR]

This paper presents the results of applied research on the Adriatic linear city (Marche, Abruzzo) aimed at building an innovative cognitive framework through GIScience methods/techniques (geodata processing, remote sensing, and scientific indexes) to support the first analytical phase of the A_GreeNet Life+ project and its aims. In particular, the paper shows the different methodological steps necessary to produce a risk map and multi-thematic geodatabase in a GIS (Geographic Information Systems) environment based on open data. The research results lay the foundations for new ways and different perspectives of investigating the urban landscape in line with the process of renewing cognitive tools for urban planning. Starting with the cognitive framework developed, the project partnership network will be scaled down for the second analysis phase aimed at prefiguring a new scenario for forestation and redeveloping the landscapevegetation system with the support of the related administrations and in accordance with the current planning framework. [ABSTRACT FROM AUTHOR]

According to Directive 2020/2184, each water supplier is required to develop a water supply risk management system, such as the World Health Organization's recommended Water Safety Plan, implement it and apply it to daily water supply practices. An important element of risk management in water supply systems (WSS) is to ensure the supply of water that is healthy and clean, that is, free of all microorganisms and parasites and any substances in quantities or concentrations that pose a potential danger to human health. Ensuring the microbiological safety of water, while minimizing the risks associated with disinfection by-products, is particularly important in extensive WSS, where water can take days to transport, and where hydraulic parameters and sediments deposited in water supply networks favor the growth of microorganisms. This paper proposes an analytical method to support the determination of areas sensitive to the loss of microbiological safety of water, while using the model to optimize the dose of disinfectant at water treatment plants and water chlorination points in the distribution subsystem. The developed methodology is part of a review of operating procedures, especially with regard to minimizing the risk of secondary microbiological contamination of water. The research model took into account the chemical as well as microbiological variability of water quality, its age, and the operational data of the WSS operation collected by the Geographic Information System (location of water quality monitoring points, spatial location of water supply infrastructure facilities), as well as the size of the population potentially exposed to water of uncertain microbiological quality. The result of the analyses is a spatial interpretation of the loss of microbiological safety of water presented in the form of risk maps. [ABSTRACT FROM AUTHOR]

About two-thirds of the population in Iceland lives in the Reykjavik capital area (RCA), which is close to active volcanoes and seismic zones. In the period 1900–2019, a total of 53 earthquakes of M w ≥ 5.0 struck in these zones. The two largest events in the Reykjanes Peninsula, M w 6.36 and M w 6.12 , occurred in 1929 and 1968, respectively. Both events were less than 20 km from the outskirts of the RCA. Late in the year 2020, the seismicity on the peninsula greatly increased due to magma intrusion and volcanic activity, which has so far resulted in three eruptions, in 2021, 2022, and 2023, and six earthquakes of M w ≥ 5.0.   Based on historical and geological data, the ongoing activity is probably the initial phase of an active period ahead that could continue for many decades, and has the potential to trigger larger earthquakes like those in 1929 and 1968. Further east, in the South Iceland Seismic Zone, two earthquakes of M w 6.52 and 6.44 struck in June 2000, and in May 2008, a M w 6.31 earthquake occurred. In both cases, around 5000 buildings were affected. Insurance loss data from these events have been used to develop empirical vulnerability models for low-rise buildings. In this study, the loss data are used to calibrate seismic vulnerability models in terms of the source-site distance. For a given magnitude scenario, this provides a simpler representation of seismic vulnerability and is useful for emergency planning and disaster management. These models are also used to compute different types of scenario risk maps for the RCA for a repeat of the 1929 earthquake. [ABSTRACT FROM AUTHOR]

Contaminated marine and coastal sediments represent the main source of secondary pollution for the aquatic environment and marine fauna, affecting, directly and indirectly, ecosystems and human health. The assessment of the distribution of chemical pollutants in marine sediments can therefore be considered a preliminary step for understanding the possible circulation of pollutants in the marine environment and planning any targeted and efficient reclamation activity. This study provides new insights on the environmental status of Bay I of Mar Piccolo basin (Southern Italy) by proposing an integrated investigation approach to define the distribution of trace metals and evaluate the thickness of the sediments potentially affected by pollution. To this aim, the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, Sn, and Zn are estimated for sediment samples collected from 19 cores, and specific environmental indices are calculated. Due to its remarkable environmental and economic relevance, the area of Taranto has been selected as a case study to evaluate the effectiveness of the proposed method in supporting the identification of hotspot areas for which priority remediation activities are needed. [ABSTRACT FROM AUTHOR]

In the last decades, climate and environmental changes have highlighted the fragility and vulnerability of the landscape, especially in mountain areas where the effects are most severe. This study promotes the methodological setup of a landscape digital twin to establish a multi-disciplinary and multi-scalar hazard overview according to a matrix framework implementable over time and space. The original contribution to the research addresses a holistic vision that combines meaningfully qualitative with quantitative approaches within a multi-hazard framework from the socio-economic perspective. This contribution presents road network risk analysis by exploiting flooding and landslide scenarios. The critical road segments or nodes most vulnerable or impacted by network performance and accessibility can be identified with minimal preprocessing from credible open-source sources. Service maps are used to show the spatial distribution of risk scores for different typologies of points of interest and hazards. Origin-destination matrix graphs display changes in travel time between facilities under various scenarios. Using a risk scores formula to generate risk maps has made it possible to effectively represent the interconnectedness among natural hazards, infrastructure, and socio-economic factors, fostering more resilient decision-making processes. The method's applicability is tested through a case study in northern Italy's Piedmont Region. [ABSTRACT FROM AUTHOR]

Ceratitis capitata (Wiedemann), the Mediterranean fruit fly or medfly, is one of the world's most serious threats to fresh fruits. It is highly polyphagous (recorded from over 300 hosts) and capable of adapting to a wide range of climates. This pest has spread to the EPPO region and is mainly present in the southern part, damaging Citrus and Prunus. In Northern and Central Europe records refer to interceptions or short‐lived adventive populations only. Sustainable programs for surveillance, spread assessment using models and control strategies for pests such as C. capitata represent a major plant health challenge for all countries in Europe. This article includes a review of pest distribution and monitoring techniques in 11 countries of the EPPO region. This work compiles information that was crucial for a better understanding of pest occurrence and contributes to identifying areas susceptible to potential invasion and establishment. The key outputs and results obtained in the Euphresco project included knowledge transfer about early detection tools and methods used in different countries for pest monitoring. A MaxEnt software model resulted in risk maps for C. capitata in different climatic regions. This is an important tool to help decision making and to develop actions against this pest in the different partner countries. [ABSTRACT FROM AUTHOR]

The sequence of seismic events occurred in last decades in Italy highlighted the high vulnerability of churches. Since the Umbria–Marche earthquake in 1997, a systematic series of post-earthquake surveys has been carried out. The data collected during those surveys were used to propose first sets of vulnerability and fragility curves. After the three last major earthquakes (L'Aquila 2009, Emilia 2012, Central Italy 2016–2017), other significant series of on-site surveys led to the collection of a large amount of data, both on the damage suffered by churches and their typological characteristics. In this study, a data set of 1127 churches damaged by the 2009 L'Aquila earthquake and the 2016–2017 Central Italy seismic sequence was analyzed. This article presents a new simplified approach to determine vulnerability indexes of churches, in order to obtain vulnerability curves, and then a set of fragility curves, depending on the typological characteristics of the single church. This allows to have a single set of curves with the entire sample and without a typological subdivision. This article first analyzes in detail the available database records to define their representativeness and influence on vulnerability. Then, a series of vulnerability modifiers are defined, pointing out which parameters effectively affect the overall vulnerability. These observations also allow identifying which parameters deserve to be collected onsite, improving the efficiency of future survey campaigns. In conclusion, starting from the vulnerability modifiers, a set of vulnerability and fragility curves are presented. [ABSTRACT FROM AUTHOR]

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Performing agricultural analysis is becoming much more effortless due to the rapid improvements in information technologies. Geographic Information Systems (GIS) provide more detailed data about climate, soil, topography, and irrigation values regarding agriculture; thus, allowing for performing detailed location analyses. These analyses cover agricultural investment maps, agricultural propriety areas, and plant pattern detections. The purpose of this study is to develop product-based agricultural risk analysis maps. Climate, soil, topography, and irrigation data are essential in the cultivation of agricultural products. With risk analysis, the risk values are determined for each risk factor. Applying the Analytical Hierarchy Process (AHP), which is one of the multi-criteria decision-making methods, the total risk value is calculated by prioritizing the risk factors. AHP is an efficient methodology developed to calculate scenario-based risk values by considering various possibilities. In this study, a model is generated by studying apricot, sour cherry, and almond farming in Ankara. As a result of the development of a GIS model for Ankara, the total risk values were mapped as "high-risk areas", "medium-risk areas", "low-risk areas" and "strongly not recommended areas" according to the points they received spatially. When the maps were examined in detail; it was determined that apricot crops in Ankara province are more sensitive to climate, soil, and topography conditions than other products. Since apricot is affected by late spring frosts, it is recommended that risk factors can be reduced by taking climatic measures in areas where soil structure is suitable. It has been determined that the sour cherry crop is less sensitive to climatic and topographic conditions and is more affected by the risk factors from the soil layers; while the almond crop is more affected by the climatic conditions, though it is more tolerant to soil conditions. According to these results, apricot can be grown in large areas with medium and high-risk levels, and in limited areas with low-risk levels. Almond with a very high-risk level can be grown in large areas compared to apricot, and sour cherry can be grown in similar-sized areas with apricot, but with a lower risk level than apricot.

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

This study aimed to analyze the impact of key causes of external and internal risk on supply chains. The basic and most probable causes of the risk are listed, based on literature research and interviews with representatives of the metal industry. The analysis was carried out by semiquantitative assessment using risk maps. The relationship between the probability of an event occurrence and its impact on supply chains was tested. The study postulates that key risk factors can be controlled through risk monitoring. Attention was drawn to the beneficial aspects of using risk maps that enable a comprehensive assessment of the situation. Both external and internal risks can cause turmoil and disruption of the supply chain. The findings suggest that external uncertainty and crises have the most direct impact on supply chain risk and are the most dangerous. The work presents the possibility of practical application of risk maps for risk assessment and monitoring. The presented approach to risk assessment complements the methodology of risk assessment and monitoring. Risk maps were used as a basic tool in assessing the impact of individual risks on supply chains. It has been found that supply chains are subject to high risk, which can be monitored through risk matrix procedures. The conducted analysis showed that critical risk areas in supply chains are external crises, environmental uncertainty, supply chain relationships, and manufacturing and the most dangerous risks in supply chains are related to external conditions beyond the control of the participants in the supply chain. The article fills a gap in research on risk monitoring in supply chains by focusing on selected, generalized measures related to industrial supply. [ABSTRACT FROM AUTHOR]

Despite promising steps towards the elimination of hepatitis C virus (HCV) in the UK, several indicators provide a cause for concern for future disease burden. We aimed to improve understanding of geographical variation in HCV-related severe liver disease and historic risk factor prevalence among clinic attendees in England and Scotland. We used metadata from 3829 HCV-positive patients consecutively enrolled into HCV Research UK from 48 hospital centres in England and Scotland during 2012–2014. Employing mixed-effects statistical modelling, several independent risk factors were identified: age 46–59 y (OR adj 3.06) and ≥60 y (OR adj 5.64) relative to <46 y, male relative to female sex (OR adj 1.58), high BMI (OR adj 1.73) and obesity (OR adj 2.81) relative to normal BMI, diabetes relative to no diabetes (OR adj 2.75), infection with HCV genotype (GT)-3 relative to GT-1 (OR adj 1.75), route of infection through blood products relative to injecting drug use (OR adj 1.40), and lower odds were associated with black ethnicity (OR adj 0.31) relative to white ethnicity. A small proportion of unexplained variation was attributed to differences between hospital centres and local health authorities. Our study provides a baseline measure of historic risk factor prevalence and potential geographical variation in healthcare provision, to support ongoing monitoring of HCV-related disease burden and the design of risk prevention measures. [ABSTRACT FROM AUTHOR]

Anthrax is a highly fatal zoonotic disease that affects all species of livestock. The study aims to develop an early warning of epidemiological anthrax using machine learning (ML) models and to study the effect of El Niño and La Niña oscillation, as well as the climate–disease relationship concerning the spatial occurrence and outbreaks in Karnataka. The disease incidence data are divided based on El Niño and La Niña events from 2004–2019 and subjected to climate-disease modeling to understand the disease pattern over the years. Machine learning models were implemented using R statistical software version 3.1.3 with Livestock density, soil profile, and meteorological and remote sensing variables as risk factors associated with anthrax incidence. Model evaluation is performed using statistical indices, viz., Cohen's kappa, receiver operating characteristic (ROC) curve, true skill statistics (TSS), etc. Models with good predictive power were combined to develop an average prediction model. The predicted results were mapped onto the Risk maps, and the Basic reproduction numbers (R0) for the districts that are significantly clustered were calculated. Early warning or risk prediction developed with a layer of R0 superimposed on a risk map helps in the preparedness for the disease occurrence, and precautionary measures before the spread of the disease. [ABSTRACT FROM AUTHOR]

Ocean noise generated by human activities at sea has been increasing over the decades, affecting marine ecosystems. Ship traffic flow between the Mediterranean or South Atlantic and northern Europe makes the coast of Portugal one of the most intense shipping highways on a global scale. Among the cetaceans of the coast of Portugal, the common dolphin (Delphinus delphis) was selected as a target species. Based on 15 years of observations, the common dolphins' habitat suitability was estimated, together with the shipping noise maps for the year 2019, to produce seasonal risk maps for the same year. A large number of areas with a high noise risk index (≥0.85) were found in Portugal's southern and southwestern coasts, especially during the summer and fall seasons. Comparably, the 0.50 risk index exceeds 7 % and 3.5 % of the total area in summer and fall, respectively. These percentages decrease to 1 % in spring and winter., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

About two-thirds of the population in Iceland lives in the Reykjavik capital area (RCA), which is close to active volcanoes and seismic zones. In the period 1900–2019, a total of 53 earthquakes of Mw≥5.0 struck in these zones. The two largest events in the Reykjanes Peninsula, Mw6.36 and Mw6.12, occurred in 1929 and 1968, respectively. Both events were less than 20 km from the outskirts of the RCA. Late in the year 2020, the seismicity on the peninsula greatly increased due to magma intrusion and volcanic activity, which has so far resulted in three eruptions, in 2021, 2022, and 2023, and six earthquakes of Mw≥5.0. Based on historical and geological data, the ongoing activity is probably the initial phase of an active period ahead that could continue for many decades, and has the potential to trigger larger earthquakes like those in 1929 and 1968. Further east, in the South Iceland Seismic Zone, two earthquakes of Mw6.52 and 6.44 struck in June 2000, and in May 2008, a Mw6.31 earthquake occurred. In both cases, around 5000 buildings were affected. Insurance loss data from these events have been used to develop empirical vulnerability models for low-rise buildings. In this study, the loss data are used to calibrate seismic vulnerability models in terms of the source-site distance. For a given magnitude scenario, this provides a simpler representation of seismic vulnerability and is useful for emergency planning and disaster management. These models are also used to compute different types of scenario risk maps for the RCA for a repeat of the 1929 earthquake.

Drought disasters are natural disasters with complex mechanisms and multiple characteristics. The quantitative evaluation of crop drought risk is central to managing drought risk and reducing loss. Therefore, assessing drought loss risk from the perspective of the drought formation process has scientific and prbactical value. In this study, we constructed crop drought loss risk curves to describe the development of drought risk loss. The intensity of drought events was expressed by the occurrence frequency, which was calculated based on long-term precipitation (1955–2012) and the copula function. The possible loss risk caused by drought was expressed as yield loss, which was simulated using the DSSAT-SROPGRO-soybean model. The spatiotemporal distribution characteristics of agricultural drought loss risk in the Huaibei Plain of Anhui Province, China, were represented by risk distribution maps. The main results showed that: 1) Drought events occurred more frequently in northern regions (Huaibei and Suzhou), but the intensity was lower. 2) Without irrigation, Fuyang was the region with the highest potential drought loss risk (more than 80% yield loss rate). 3) Irrigation had a significant effect on reducing drought risk loss, while efficiency was influenced by the spatiotemporal distribution of precipitation. This study has established and implemented a quantitative framework for regional drought risk assessment by creating drought risk curves and risk maps, which have significant value for improving the regional agricultural drought risk management level. [ABSTRACT FROM AUTHOR]

Performing agricultural analysis is becoming much more effortless due to the rapid improvements in information technologies. Geographic Information Systems (GIS) provide more detailed data about climate, soil, topography, and irrigation values regarding agriculture; thus, allowing for performing detailed location analyses. These analyses cover agricultural investment maps, agricultural propriety areas, and plant pattern detections. The purpose of this study is to develop product-based agricultural risk analysis maps. Climate, soil, topography, and irrigation data are essential in the cultivation of agricultural products. With risk analysis, the risk values are determined for each risk factor. Applying the Analytical Hierarchy Process (AHP), which is one of the multi-criteria decision-making methods, the total risk value is calculated by prioritizing the risk factors. AHP is an efficient methodology developed to calculate scenario-based risk values by considering various possibilities. In this study, a model is generated by studying apricot, sour cherry, and almond farming in Ankara. As a result of the development of a GIS model for Ankara, the total risk values were mapped as "high-risk areas", "medium-risk areas", "low-risk areas" and "strongly not recommended areas" according to the points they received spatially. When the maps were examined in detail; it was determined that apricot crops in Ankara province are more sensitive to climate, soil, and topography conditions than other products. Since apricot is affected by late spring frosts, it is recommended that risk factors can be reduced by taking climatic measures in areas where soil structure is suitable. It has been determined that the sour cherry crop is less sensitive to climatic and topographic conditions and is more affected by the risk factors from the soil layers; while the almond crop is more affected by the climatic conditions, though it is more tolerant to soil conditions. According to these results, apricot can be grown in large areas with medium and high-risk levels, and in limited areas with low-risk levels. Almond with a very high-risk level can be grown in large areas compared to apricot, and sour cherry can be grown in similar-sized areas with apricot, but with a lower risk level than apricot. [ABSTRACT FROM AUTHOR]

Climate change will expose the food‐producing sector to a range of challenges. Inland aquaculture farms are particularly vulnerable, due to the difficulty in changing their location, and therefore require specific tools to predict the influence of direct and indirect effects on production, environment and economic feasibility. The objective of our study was to apply a simple set of models to produce a set of growth, risk and suitability maps for stakeholders within the common carp sector in Poland, to assist decision‐making under two different scenarios of climate change: a moderate situation (RCP 4.5) and an extreme situation (RCP 8.5). We used present (2000–2019) and future projections (2080–2099) for water surface temperature based on land surface temperature data from regionally downscaled climate models to draw maps to: (i) show optimal temperature conditions for carp growth, (ii) assess risk of disease outbreak caused by three important common carp pathogens: Cyprinid herpesvirus 3 (CyHV‐3), carp oedema virus (CEV) and spring viremia of carp (SVCV) and (iii) predict potential suitability changes of carp farming in Poland. The study identified areas with the most and least favourable temperature conditions for carp growth, as well as those areas with the highest/lowest number of days with suitable temperatures for virus infection. These suitability maps showed the combined effect of direct and indirect effects of climate change projections under RCP 8.5 and RCP 4.5 scenarios. The approach applied herein will be of use worldwide for analysing the risks of temperature increase to land‐based aquaculture, and the results presented are important for carp farmers in Poland and elsewhere, industry in general, and government stakeholders, to understand the direct and indirect effects of climate change on the triple bottom line of people, planet, and profit. [ABSTRACT FROM AUTHOR]

• Risk assessment was estimated starting from environmental hazard data and vulnerability and • exposure information of architectural buildings. • Three different risk levels can be assessed starting from air pollution and meteorological data measured by national monitoring station network. • Vulnerability and exposure describe the conservation condition based on damage intensity. • Risk assessment as a tool to plan maintenance and restoration activities according a priority scale. Limestone monuments are generally subjected to natural and anthropogenic environmental threats. This paper describes a methodology to assess the risk of surface recession damage, due to air pollution and meteorological factors, to architectural buildings in Italy. Through the elaboration of environmental data and information on conservation conditions of cultural heritage, three different risk models were identified: i) the territorial risk, obtained combining territorial hazard information with the density of historic buildings defined at municipal scale; ii) the individual risk, attained by overlapping territorial hazard data at municipal scale, with surface vulnerability and exposure information of a single monument; iii) the local risk, defined starting from the combination of territorial hazard at sub-municipal scale, with vulnerability and exposure information of a single monument. The first model refers to the risk of an aggregate of cultural assets located in a municipality, while the other two indicators identify the damage risk of a single monument at municipal and sub-municipal spatial scales. The proposed method was applied, using GIS (Geographical Information System) application, to identify the three risk models for Italian architectural buildings using environmental and monument conservation data updated to 2018. The territorial hazard was defined by assessing the material loss damage expressed as surface recession and quantified starting from air pollutant concentrations and meteorological data measured by the monitoring station network of Italy in 2018. The vulnerability and exposure information were extracted from the Italian Risk Map of Cultural Heritage system. The obtained results allowed to identify the potentially most aggressive areas from climatic and environmental point of view and the architectural buildings most exposed to surface recession risk. The methodology described in this paper can be therefore considered as a tool that can be used, by policy makers, to plan priority maintenance and conservation activities in order to reduce the progress and the intensity of decay on outdoor cultural properties. [Display omitted] [ABSTRACT FROM AUTHOR]

The risks to potable aquifers due to brine leakage through plugged and abandoned (P&A) wells is highly uncertain and a potentially significant contributor to the risk profile in Geologic Carbon Storage (GCS). This uncertainty stems from the unknown location of wells and the large variance of P&A wellbore permeability, making the spatial assessment of P&A brine leakage risk challenging. A new methodology is presented to generate “risk maps”, or spatial distributions of brine leakage risk to groundwater resources as defined with no-impact or Maximum Contaminant Level (MCL) thresholds. The methodology utilizes probability theory, thereby avoiding the use of computationally expensive Monte Carlo simulations while maintaining flexibility in modeling techniques. These maps provide quantitative probabilities of risk as a function of time to inform site selection and monitoring during and post-injection, conducive to the US EPA's permitting of class-VI wells and the so-called “area of review”, AoR. As a demonstration of the methodology, a numerical model of a hypothetical fully-coupled system spanning from the injection reservoir to the USDW is used to assess the evolution of brine leakage through P&A wells. Risk maps of CO2 leakage can also be generated with this methodology for a comprehensive assessment of GCS leakage risk.

The risks to potable aquifers due to brine leakage through plugged and abandoned (P&A) wells is highly uncertain and a potentially significant contributor to the risk profile in Geologic Carbon Storage (GCS). This uncertainty stems from the unknown location of wells and the large variance of P&A wellbore permeability, making the spatial assessment of P&A brine leakage risk challenging. A new methodology is presented to generate “risk maps”, or spatial distributions of brine leakage risk to groundwater resources as defined with no-impact or Maximum Contaminant Level (MCL) thresholds. The methodology utilizes probability theory, thereby avoiding the use of computationally expensive Monte Carlo simulations while maintaining flexibility in modeling techniques. These maps provide quantitative probabilities of risk as a function of time to inform site selection and monitoring during and post-injection, conducive to the US EPA's permitting of class-VI wells and the so-called “area of review”, AoR. As a demonstration of the methodology, a numerical model of a hypothetical fully-coupled system spanning from the injection reservoir to the USDW is used to assess the evolution of brine leakage through P&A wells. Risk maps of CO2 leakage can also be generated with this methodology for a comprehensive assessment of GCS leakage risk.

Railway transportation, integral to Canada's supply chain, is recognized for its reliability and safety, yet its complexity introduces various risks. In this study, a meteorological risk assessment of the Canadian transcontinental freight railway is performed using a comprehensive spatial analysis. Flood (areas prone to flood risk across the province), rain (maximum daily precipitation in mm), snow (maximum snowfall in cm), minimum temperature (minimum temperature in Celsius), and wind (maximum gust speed in Km/h) have been selected as factors to generate meteorological risk maps of the Transcontinental Freight Canadian National Railway (CN) for the Saskatchewan and Ontario provinces. The study generated five integrated risk maps, varying in factor weighting approaches, including equal weight, score-based weighting, expert opinion-based Analytical Hierarchy Process, and seasonal considerations for both warm and cold seasons. These risk maps demonstrate hotspots and hazardous areas that require more attention and planning to maintain the continuity of the supply chain. The results of this study can be used to enhance safety, reduce service disruptions, and ensure the smooth operation of the railway network. [ABSTRACT FROM AUTHOR]

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation. • The EIA outbreaks in Europe exhibited five significant spatiotemporal clusters. • The hotspots concentrated in southeastern France and northwestern Italy. • The model identified high-risk areas for the occurrence of EIA in Europe. • The model revealed key factors influencing the occurrence of EIA in Europe. • The study can provide decision-makers with valuable insights for EIA control. [ABSTRACT FROM AUTHOR]

Reducing the use of pesticides in agricultural systems is a prerequisite for sustainable agriculture and, therefore, knowledge on the factors that influence the regional insect pest densities is necessary. Based on multi-site and multi-annual observations of the cabbage stem weevil [Ceutorhynchus pallidactylus (Marsham, 1802)] in winter oilseed rape (Brassica napus Linnaeus) and the corresponding meteorological measurements, a statistical relationship for forecasting the abundance was derived. The model explains 84% of the variation of the data set. The remaining 16% might be explained by the landscape effects and agricultural practices, such as crop protection. Based on the statistical relationship between the mean winter air temperature and the abundance of the cabbage stem weevil in the winter oilseed rape, risk maps were derived as a forecast tool for practical farming.

Abstract Background Declining malaria prevalence and pressure on external funding have increased the need for efficiency in malaria control in sub-Saharan Africa (SSA). Modelled Plasmodium falciparum parasite rate (PfPR) maps are increasingly becoming available and provide information on the epidemiological situation of countries. However, how these maps are understood or used for national malaria planning is rarely explored. In this study, the practices and perceptions of national decision-makers on the utility of malaria risk maps, showing prevalence of parasitaemia or incidence of illness, was investigated. Methods A document review of recent National Malaria Strategic Plans was combined with 64 in-depth interviews with stakeholders in Kenya, Malawi and the Democratic Republic of Congo (DRC). The document review focused on the type of epidemiological maps included and their use in prioritising and targeting interventions. Interviews (14 Kenya, 17 Malawi, 27 DRC, 6 global level) explored drivers of stakeholder perceptions of the utility, value and limitations of malaria risk maps. Results Three different types of maps were used to show malaria epidemiological strata: malaria prevalence using a PfPR modelled map (Kenya); malaria incidence using routine health system data (Malawi); and malaria prevalence using data from the most recent Demographic and Health Survey (DRC). In Kenya the map was used to target preventative interventions, including long-lasting insecticide-treated nets (LLINs) and intermittent preventive treatment in pregnancy (IPTp), whilst in Malawi and DRC the maps were used to target in-door residual spraying (IRS) and LLINs distributions in schools. Maps were also used for operational planning, supply quantification, financial justification and advocacy. Findings from the interviews suggested that decision-makers lacked trust in the modelled PfPR maps when based on only a few empirical data points (Malawi and DRC). Conclusions Maps were generally used to identify areas with high prevalence in order to implement specific interventions. Despite the availability of national level modelled PfPR maps in all three countries, they were only used in one country. Perceived utility of malaria risk maps was associated with the epidemiological structure of the country and use was driven by perceived need, understanding (quality and relevance), ownership and trust in the data used to develop the maps.

Flood maps group different types of cartographies related to flooding and the components and variables of flood risk and its mitigation measures. This paper analyses the most important facts in the development of flood mapping in Spain and assesses the current. While 60/2007 EU Directive has been an important step for mapping risk, future developments must: (i) overcome the concept of return period; (ii) incorporate other aspects of the European Directive, basically vulnerability and susceptibility to flooding from rainwater, also the effect of climate change on flood hazards; (iii) include scenarios for the consequences of climate change; (iv) incorporate risk cartography as a key element of 'green infrastructure', like tool in spatial planning; (v) Reduce the map representation scale; (vi) incorporate new elements within risk maps to improve emergency management; (vii) improve public-private cooperation; (viii) facilitate the legal use of hazard and risk maps in administrative and court processes. [ABSTRACT FROM AUTHOR]

Built heritage in Oman faces many natural and cultural risks. Socio-economic development has negatively affected the built heritage in Bawshar, one of Oman's most developed cities. This study identifies and measures the current and potential risks to built heritage in Bawshar, focusing on vegetation, bulldozing, and urban change-related risks between 2000 and 2017. It also provides baseline data to help heritage managers protect the sites most vulnerable to risks. Satellite imagery and geographic information systems were used to classify change-related risks to built heritage. Risk maps were produced for the built heritage sites under threat over different periods in Bawshar. The results showed that 60%, 28%, and 11% of very high- and high-risk zones faced bulldozing, vegetation, and urban change-related risks, respectively. The methodology adopted in this study can help measure the type and magnitude of risks to built heritage in cities with characteristics similar to those of Bawshar. [ABSTRACT FROM AUTHOR]

Introduction: Marine mammals are affected by diverse threats that must be identified for mitigation efforts. Objective: To visually quantify threats to marine mammals in Colombia, and to identify mitigation actions. Methods: We georeferenced threats based on the literature, covering 35 species in the period 1991-2020, and superimposed them on species range maps. Results: 22 species had at least one threat. Bycatch and interactions with fishing gear affected 16 species, followed by direct capture (8 species), vessel traffic/transit (6 species) and alteration of ocean physics (6 species). The most affected species, in terms of the greatest diversity of threats, are: humpback whale (Megaptera novaeangliae), Caribbean West Indian manatee (Trichechus manatus), marine tucuxi (Sotalia guianensis) and bottlenose dolphin (Tursiops truncatus). Nearly all of Colombia’s marine territory presents some degree of risk for marine mammals. High-risk areas are Buenaventura, Guapi, Cupica Gulf and Tumaco in the Pacific; and Urabá Gulf, Darién Gulf, Morrosquillo Gulf, off Barranquilla, Ciénaga Grande de Santa Marta and Coquivacoa Gulf in the Caribbean. Conclusion: Marine mammals in Colombia are currently at risk due to several threats, especially related to fishing, hunting/capture and shipping activities, mainly in coastal areas. Urgent evaluation and management actions are needed in the ten high-risk areas identified in this study. [ABSTRACT FROM AUTHOR]

Natural disasters are events that negatively affect human life and cause material and moral damages. As in the rest of the world, one of the biggest natural disasters after earthquakes in our country is floods. The flood event turns into a disaster in cases caused by human interventions such as the change of the river bed, the increase in the construction on the riversides, and the blocking of the river perpendicular to the flow direction. In this study, risk maps for floods with different return intervals (25, 50, 100 years) were produced with GIS for Ayancık stream in Sinop province. As a result, when the flood risk maps were analyzed, the regions located in the floodplain were considered very risky regions. According to the results obtained, the flood disaster reaches a depth of 7 meters. Ayancık Stream and its surroundings are a very risky area exposed to floods as they have a maximum depth of 3.5 m. In addition, flood risk areas with 50 and 100-year return period floods, increase 3.71 and 12.83 percent, respectively, from 25-year return period flood risk areas. In addition, it was concluded that the majority of the areas affected by the floods in all three turning ranges within the scope of the study area are located in the very risky region, while the extreme risk areas are less than the other risky groups. [ABSTRACT FROM AUTHOR]

Phytophthora cinnamomi is among the most destructive plant pathogens that affect many host plants in different ecosystems. Economically important hosts of this pathogen include avocado (Persea americana), and this oomycete may cause large-scale destructive epidemics. This study analyzed the potential geographic distribution of P. cinnamomi (pathogen) and avocado (host), and distribution of the pathogen in relation to multiple hosts in the Americas. Niche overlap between hosts and pathogen were also evaluated, using a multistep process of ecological niche modelling and the MaxEnt algorithm. Niche similarity among pathogen populations in different hosts and related niche similarity were also examined. As a complement, a tool was designed to visualize the risk of this pathogen in avocado. Results showed that the pathogen was randomly distributed in the avocado niche environmental space, but the niche of the pathogenwas narrower than that of its principal host. The niche of the pathogen was largely a function of the host niches. Areas with potential to grow avocado could present low risk of P. cinnamomi, but given the invasiveness of this pathogen, they may be affected in the future.

We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015–December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.

Stakeholder engagement in research is widely advocated as a tool to integrate diverse knowledge and perspectives in the management of health threats while addressing potential conflicts of interest. Although guidelines for stakeholder engagement exist in public health and environmental sciences, the feasibility of actionable decisions based on scientific analyses and the lessons learned from the stakeholder engagement in the process co-creation of knowledge have been rarely discussed in One Health literature and veterinary sciences. Risk maps and risk regionalization using spatiotemporal epidemiological/analytical tools are known to improve risk perception and communication. Risk maps are useful when informing policy and management decisions on quarantine, vaccination, and surveillance intended to prevent or control threats to human, animal, or environmental health interface (i.e., One Health). We hypothesized that researcher-stakeholder engagement throughout the research process could enhance the utility of risk maps; while identifying opportunities to improve data collection, analysis, interpretation, and, ultimately, implementation of scientific/evidence-based management and policy measures. Three case studies were conducted to test this process of co-creation of scientific knowledge, using spatiotemporal epidemiological approaches, all related to One Health problems affecting Minnesota. Our interpretation of the opportunities, challenges, and lessons learned from the process are summarized from both researcher and stakeholder perspectives. By sharing our experience we intend to provide an understanding of the expectations, realizations, and “good practices” we learned through this slow-moving iterative process of co-creation of knowledge. We hope this contribution benefits the planning of future transdisciplinary research related to risk map-based management of One Health problems.