Individual-level models of disease transmission incorporating piecewise spatial risk functions.

Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information. Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk. We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them to produce results in line with, or superior to, parametric spatial ILMs. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease.

Livestock-associated spatial risk factors for human salmonellosis and campylobacteriosis.

AIMS: Most human infections with non-typhoid Salmonella (NTS) or Campylobacter are zoonotic in nature and acquired though consumption of contaminated food of mainly animal origin. However, individuals may also acquire salmonellosis or campylobacteriosis through non-foodborne transmission pathways, such as those mediated by the environment. This emphasizes the need to consider both direct and indirect exposure to livestock sources as a possible transmission route for NTS and Campylobacter. Therefore, this study aimed at assessing whether salmonellosis and campylobacteriosis incidence is spatially associated with exposure to livestock (i.e. small ruminants, dairy cows, veal calves, laying hens, broiler chickens and pigs) in the Netherlands for the years 2007-2019 and 2014-2019 respectively. METHODS AND RESULTS: Risk factors (population-weighted number of animals) and their population attributable fractions were determined using a Poisson regression model with a log-link function fitted using integrated nested Laplace approximation. The analyses were performed for different hexagonal sizes (90, 50, 25 and 10 km2) and accounted for geographical coverage of the diagnostic laboratory catchment areas. Moreover, serological data were used to look into the possible effects of acquired immunity due to repeated exposure to the pathogen through the environment that would potentially hinder the analyses based on the incidence of reported cases. A linear mixed-effects model was then fitted where the postal code areas were included as a random effect. Livestock was not consistently significantly associated with acquiring salmonellosis or campylobacteriosis in the Netherlands. CONCLUSIONS: Results showed that living in livestock-rich areas in the Netherlands is not a consistently significant, spatially restricted risk factor for acquiring salmonellosis or campylobacteriosis, thereby supporting current knowledge that human infections with Salmonella and Campylobacter are mainly foodborne.

Crop use structures resource selection strategies for African elephants in a human-dominated landscape.

To conserve wide-ranging species in degraded landscapes, it is essential to understand how the behavior of animals changes in relation to the degree and composition of modification. Evidence suggests that large inter-individual variation exists in the propensity for use of degraded areas and may be driven by both behavioral and landscape factors. The use of cultivated lands by wildlife is of particular interest, given the importance of reducing human-wildlife conflicts and understanding how such areas can function as biodiversity buffers. African elephant space use can be highly influenced by human activity and the degree to which individuals crop-raid. We analyzed GPS data from 56 free-ranging elephants in the Serengeti-Mara Ecosystem using resource selection functions (RSFs) to assess how crop use may drive patterns of resource selection and space use within a population. We quantified drivers of similarity in resource selection across individuals using proximity analysis of individual RSF coefficients derived from random forest models. We found wide variation in RSF coefficient values between individuals indicating strongly differentiated resource selection strategies. Proximity assessment indicated the degree of crop use in the dry season, individual repeatability, and time spent in unprotected areas drove similarity in resource selection patterns. Crop selection was also spatially structured in relation to agricultural fragmentation. In areas with low fragmentation, elephants spent less time in crops and selected most strongly for crops further from protected area boundaries, but in areas of high fragmentation, elephants spent twice as much time in crops and selected most strongly for crops closer to the protected area boundary. Our results highlight how individual differences and landscape structure can shape use of agricultural landscapes. We discuss our findings in respect to the conservation challenges of human-elephant conflict and incorporating behavioral variation into human-wildlife coexistence efforts.

Oceanographic and anthropogenic variables driving marine litter distribution in Mediterranean protected areas: Extensive field data supported by forecasting modelling.

Marine litter concentration in the Mediterranean Sea is strongly influenced both by anthropogenic pressures and hydrodynamic factors that locally characterise the basin. Within the Plastic Busters MPAs (Marine Protected Areas) Interreg Mediterranean Project, a comprehensive assessment of floating macro- and microlitter in the Pelagos Sanctuary and the Tuscan Archipelago National Park was performed. An innovative multilevel experimental design has been planned ad-hoc according to a litter provisional distribution model, harmonising and implementing the current sampling methodologies. The simultaneous presence of floating macro- and microlitter items and the potential influences of environmental and anthropogenic factors affecting litter distribution have been evaluated to identify hotspot accumulation areas representing a major hazard for marine species. A total of 273 monitoring transects of floating macrolitter and 141 manta trawl samples were collected in the study areas to evaluate the abundance and composition of marine litter. High mean concentrations of floating macrolitter (399 items/km2) and microplastics (259,490 items/km2) have been found in the facing waters of the Gulf of La Spezia and Tuscan Archipelago National Park as well in the Genova canyon and Janua seamount area. Accordingly, strong litter inputs were identified to originate from the mainland and accumulate in coastal waters within 10-15 nautical miles. Harbours and riverine outfalls contribute significantly to plastic pollution representing the main sources of contamination as well as areas with warmer waters and weak oceanographic features that could facilitate its accumulation. The results achieved may indicate a potentially threatening trend of litter accumulation that may pose a serious risk to the Pelagos Sanctuary biodiversity and provide further indications for dealing with plastic pollution in protected areas, facilitating future management recommendations and mitigation actions in these fragile marines and coastal environments.

An ensemble modeling approach to predict spatial risk patches of the Persian leopard-livestock conflicts in Lorestan Province, Iran.

This study was conducted in the Lorestan Province in the west of Iran with two objectives of identifying major environmental variables in spatial risk modeling and identifying spatial risk patches of livestock predation by the Persian leopard. An ensemble approach of three models of maximum entropy (MaxEnt), generalized boosting model (GBM), and random forest (RF) were applied for spatial risk modeling. Our results revealed that livestock density, distance to villages, forest density, and human population density were the most important variables in spatial risk modeling of livestock predation by the leopard. The center of the study area had the highest probability of livestock predation by the leopard. Ten spatial risk patches of livestock predation by the leopard were identified in the study area. In order to mitigate the revenge killing of the leopards, the findings of this study highlight the imperative of implementing strategies by the Department of Environment (DoE) to effectively accompany the herds entering the wildlife habitats with shepherds and a manageable number of guarding dogs. Accordingly, the identified risk patches in this study deserve considerable attention, especially three primary patches found in the center and southeast of Lorestan Province.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results: Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Discussion: Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Climate-Related Risk Modeling of Banana Xanthomonas Wilt Disease Incidence in the Cropland Area of Rwanda.

Banana Xanthomonas wilt (BXW) is a major threat to banana production in Rwanda, causing up to 100% yield loss. There are no biological or chemical control measures, and little is known about the potential direction and magnitude of its spread; hence, cultural control efforts are reactive rather than proactive. In this study, we assessed BXW risk under current and projected climates to guide early warning and control by applying the maximum entropy (Maxent) model on 1,022 georeferenced BXW datapoints and 20 environmental variables. We evaluated the significance of variables and mapped potential risk under current and future climates to assess spatial dynamics of the disease distribution. BXW occurrence was reliably predicted (mean validation AUC values ranging from 0.79 to 0.85). Precipitation of the coldest quarter, average maximum monthly temperature, annual precipitation, and elevation were the strongest predictors, which were responsible for 22.1, 13, 12.6, and 9.4% of the observed incidence variability, respectively, while mean temperature of the coldest quarter had the highest gain in isolation. Furthermore, the most susceptible regions (western, northern, and southern Rwanda) were characterized by elevation (1,350 to 2,000 m), annual precipitation (900 to 1,700 mm), and average temperature (14 to 20°C), among other variables, suggesting that a consistent, rainy, and warm climate is more favorable for BXW spread. Under the future climate, the risk was predicted to increase and spread to other regions. We conclude that climate change will likely exacerbate BXW-related losses of banana land area and yield under the influence of temperature and moisture. Our findings support evidence-based targeting of extension service delivery to farmers and national early warning for timely action.

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target.

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China.

Introduction: COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information. Methods: Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk. Results: Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points. Discussion: Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.

Shared geographic spatial risk of childhood undernutrition in Malawi: An application of joint spatial component model.

Objectives: This study aimed at assessing shared spatial risk of childhood undernutrition indicators in Malawi. Study design: Cross-sectional design. Methods: The shared spatial component model was fitted to childhood undernutrition indicators, namely: stunting, wasting and underweight, using 5066 child records of the 2015/16 Malawi demographic health survey data. The spatial components were districts, and were modeled by the convolution prior, with the structured components being assigned the conditional autoregressive distribution. Results: There is significant clustering of shared spatial risk of stunting and wasting (Moran I = 0.464, p-value = 0.009), and wasting and underweight (Moran I = 0.392, p-value = 0.026), and the risk maps show southern districts, followed by central districts being at greater risk of jointly having stunting and wasting, wasting and underweight, compared to the northern region districts. The shared spatial risk of stunting and underweight is randomly dispersed across the country (Moran I = - 0.044, p-value = 0.539). Conclusion: Interventions to reduce the shared risk of child undernutrition should focus on the southern region districts and those in the central region, and a suggestion is made to address the issue of overpopulation and effects of climate change.

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

Assessment of the spatiotemporal risk of avian influenza between waterfowl and poultry farms during the annual cycle: A spatial prediction study focused on seasonal distribution changes in resident waterfowl in South Korea.

Previous studies and efforts to prevent and manage avian influenza (AI) outbreaks have mainly focused on the wintering season. However, outbreaks of AI have been reported in the summer, including the breeding season of waterfowl. Additionally, the spatial distribution of waterfowl can easily change during the annual cycle due to their life-cycle traits and the presence of both migrants and residents in the population. Thus, we assessed the spatiotemporal variation in AI exposure risk in poultry due to spatial distribution changes in three duck species included in both major residents and wintering migrants in South Korea, the mandarin, mallard and spot-billed duck, during wintering (October-March), breeding (April-June) and whole annual seasons. To estimate seasonal ecological niche variations among the three duck species, we applied pairwise ecological niche analysis using the Pianka index. Subsequently, seasonal distribution models were projected by overlaying the monthly ranges estimated by the maximum entropy model. Finally, we overlaid each seasonal distribution range onto a poultry distribution map of South Korea. We found that the mandarin had less niche overlap with the mallard and spot-billed duck during the wintering season than during the breeding season, whereas the mallard had less niche overlap with the mandarin and spot-billed duck during the breeding season than during the wintering season. Breeding and annual distribution ranges of the mandarin and spot-billed duck, but not the mallard, were similar or even wider than their wintering ranges. Similarly, the mandarin and spot-billed duck showed more extensive overlap proportions between poultry and their distributional ranges during both the breeding and annual seasons than during the wintering season. These results suggest that potential AI exposure in poultry can occur more widely in the summer than in winter, depending on sympatry with the host duck species. Future studies considering the population density and variable pathogenicity of AI are required.

Mapping the risk distribution of Borrelia burgdorferi sensu lato in China from 1986 to 2020: a geospatial modelling analysis.

Lyme borreliosis, recognized as one of the most important tick-borne diseases worldwide, has been increasing in incidence and spatial extent. Currently, there are few geographic studies about the distribution of Lyme borreliosis risk across China. Here we established a nationwide database that involved Borrelia burgdorferi sensu lato (B. burgdorferi) detected in humans, vectors, and animals in China. The eco-environmental factors that shaped the spatial pattern of B. burgdorferi were identified by using a two-stage boosted regression tree model and the model-predicted risks were mapped. During 1986-2020, a total of 2,584 human confirmed cases were reported in 25 provinces. Borrelia burgdorferi was detected from 35 tick species with the highest positive rates in Ixodes granulatus, Hyalomma asiaticum, Ixodes persulcatus, and Haemaphysalis concinna ranging 20.1%-24.0%. Thirteen factors including woodland, NDVI, rainfed cropland, and livestock density were determined as important drivers for the probability of B. burgdorferi occurrence based on the stage 1 model. The stage 2 model identified ten factors including temperature seasonality, NDVI, and grasslands that were the main determinants used to distinguish areas at high or low-medium risk of B. burgdorferi, interpreted as potential occurrence areas within the area projected by the stage 1 model. The projected high-risk areas were not only concentrated in high latitude areas, but also were distributed in middle and low latitude areas. These high-resolution evidence-based risk maps of B. burgdorferi was first created in China and can help as a guide to future surveillance and control and help inform disease burden and infection risk estimates.

Microplastic abundance and biodiversity richness overlap: Identification of sensitive areas in the Western Ionian Sea.

Plastic pollution in the Mediterranean Sea has been widely reported, but its impact on biodiversity has not been fully explored. Simultaneous sampling of microplastics (MP) with a manta net and surveys of large marine vertebrates were conducted along the coastal waters of Sicily (Western Ionian Sea). A total of 17 neustonic samples have been collected and 17 marine species (cetaceans, sea turtles, seabirds, and fish) have been sighted in the target area. Kernel density estimation was evaluated to highlight a possible overlap between the presence of large marine fauna and MP densities to provide a preliminary risk assessment. The highest biodiversity and MP concentration (0.197 ±â€¯0.130 items/m2) were observed in the southernmost part of the studied area. The overlap between biodiversity hotspots and the occurrence of MP, potential contribute to the identification of sensitive areas of exposure in a poorly studied region.

Modelling the temperature suitability for the risk of West Nile Virus establishment in European Culex pipiens populations.

Increases in temperature and extreme weather events due to global warming can create an environment that is beneficial to mosquito populations, changing and possibly increasing the suitable geographical range for many vector-borne diseases. West Nile Virus (WNV) is a flavivirus, maintained in a mosquito-avian host cycle that is usually asymptomatic but can cause primarily flu-like symptoms in human and equid accidental hosts. In rare circumstances, serious disease and death are possible outcomes for both humans and horses. The main European vector of WNV is the Culex pipiens mosquito. This study examines the effect of environmental temperature on WNV establishment in Europe via Culex pipiens populations through use of a basic reproduction number ( R 0 ${R_0}$ ) model. A metric of thermal suitability derived from R 0 ${R_0}$ was developed by collating thermal responses of different Culex pipiens traits and combining them through use of a next-generation matrix. WNV establishment was determined to be possible between 14°C and 34.3°C, with the optimal temperature at 23.7°C. The suitability measure was plotted against monthly average temperatures in 2020 and the number of months with high suitability mapped across Europe. The average number of suitable months for each year from 2013 to 2019 was also calculated and validated with reported equine West Nile fever cases from 2013 to 2019. The widespread thermal suitability for WNV establishment highlights the importance of European surveillance for this disease and the need for increased research into mosquito and bird distribution.

A machine learning approach for modelling the occurrence of Galba truncatula as the major intermediate host for Fasciola hepatica in Switzerland.

Fasciolosis caused by the trematode Fasciola hepatica is an important parasitosis in both livestock and humans across the globe. Chronic infections in cattle are associated with considerable economic losses. As a prerequisite for an effective control and prevention of fasciolosis in cattle fine-scale predictive models on farm-level are needed. Since disease transmission will only occur where the mollusc intermediate host is present, the objective of our research was to develop a regression model that allows to predict the local presence or absence of Galba truncatula as principal intermediate host for Fasciola hepatica in Switzerland. By implementing generalized linear mixed models (GLMMs) a total amount of 70 variables were analysed for their potential influence on the likelihood πi of finding Galba truncatula at a certain site. Important site-specific features could be considered by selecting suitable modelling procedures. The statistical software R was used to conduct regression analysis, performing the grplasso and the glmmLasso method. The selection of parameters was based on 10-fold cross validation and the Bayesian Information Criterion (BIC). This yielded a total number of 19 potential predictor variables for the grplasso and 13 variables for the glmmLasso model, which also included random effects. Nine variables appeared to be relevant predictors for the occurrence of Galba truncatula in both models. These included reed/humid area, spring water, water bodies within a 100 m radius, and trees/bushes as powerful positive predictors. High soil depth, temperatures frequently exceeding 30 °C in the year preceding the search for snails and temperatures below 0 °C especially in the second year before were identified to exert an adverse effect on the occurrence of Galba truncatula. Temperatures measured near ground level proved to be more powerful predictors than macroclimatic parameters. Precipitation values seemed to be of minor impact in the given setting. Both regression models may be convenient for a fine-scale prediction of the occurrence of Galba truncatula, and thus provide useful approaches for the development of future spatial transmission models, mapping the risk of fasciolosis in Switzerland on farm-level.

A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach.

COVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings. Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model. The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 µm-700 µm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach.

In 2016, the British government acknowledged the importance of reducing antimicrobial prescriptions to avoid the long-term harmful effects of overprescription. Prescription needs are highly dependent on the factors that have a spatiotemporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyze data by searching for group structures and therefore identifying peer groups of GPs with similar behavior. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatiotemporal clustering approach for modeling the behavior of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a Kernel density estimation-based process. This makes our approach suitable for cases with different local densities, which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behavior within a group of similar peers and detect regions of extreme behaviors.

Spatial ecology of conflicts: unravelling patterns of wildlife damage at multiple scales.

Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damage to agriculture and livestock. Spatial risk modelling is a useful tool to gain the understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damage. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damage and help mitigate conflicts in a proactive and preventive manner.

A spatial prioritization method for identifying potential eco-risk distributions of heavy metals in soil and birds.

Geochemical approaches are popular for evaluations based on heavy metal concentrations in sediments or soils for eco-risk assessment. This study proposes a systematic geochemical approach (SymGeo) to explore six heavy metals in topsoils and bird tissues and organs of the target birds. We assume that the proposed approach based on field-collected heavy metals in topsoils and feathers can predict the areas with the potential risk of the heavy metals in birds. Finite mixture distribution modeling (FMDM) was used to identify background values of the heavy metal concentrations in topsoil. A spatial enrichment factor (EF), potential contamination index (PCI), contamination degree (Cod), and potential ecological risk index (PRI) based on FMDM results for topsoil, and a potential risk index (PRIbird) of heavy metals in the birds, were utilized for systematic prioritization of high eco-risk areas. Using multiple EF, PRI, and Cod results and multiple PRI-based maps of the heavy metals in feathers, we systematically prioritized risk areas where there is a high potential for heavy metal contamination in the birds. Our results indicate that heavy metal concentrations in the feather, liver, and kidney are not spatially cross-autocorrelated but are statistically significantly correlated with some heavy metals in topsoil due to external and internal depositions. Further, multiple EF, Cod, and RI distributions for topsoil, along with the PRI of the feather, showed that adequate coverages for potential risk for birds were greater than 71.05% in the top 30% and 84.69% in the top 20% potential eco-risk priority area of heavy metals in bird liver and kidney. Hence, our proposed approach suggests that assessments of heavy metals in bird feathers and topsoils without bird organs can be utilized to identify spatially high-risk areas. The proposed approach could be improved by incorporating water and sediment samples to enhance the crowdsourcing and the species-specific data.