scholarly journals Spatiotemporal patterns and agroecological risk factors for cutaneous and renal glomerular vasculopathy (Alabama Rot) in dogs in the UK

2018 ◽  
Vol 183 (16) ◽  
pp. 502-502 ◽  
Author(s):  
Kim B Stevens ◽  
Rosanne Jepson ◽  
Laura Phillipa Holm ◽  
David John Walker ◽  
Jacqueline Martina Cardwell
Keyword(s):  
Seasonal Pattern ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Relative Contribution ◽  
Annual Reporting ◽  
Climatic Drivers ◽  
Maximum Temperatures ◽  
The Uk ◽  
Permutation Statistic

The annual outbreaks of cutaneous and renal glomerular vasculopathy (CRGV) reported in UK dogs display a distinct seasonal pattern (November to May) suggesting possible climatic drivers of the disease. The objectives of this study were to explore disease clustering and identify associations between agroecological factors and CRGV occurrence. Kernel-smoothed maps were generated to show the annual reporting distribution of CRGV, Kuldorff’s space–time permutation statistic used to identify significant spatiotemporal case clusters and a boosted regression tree model developed to quantify associations between CRGV case locations and a range of agroecological factors. The majority of diagnoses (92 per cent) were reported between November and May while the number of regions reporting the disease increased between 2012 and 2017. Two significant spatiotemporal clusters were identified—one in the New Forest during February and March 2013, and one adjacent to it (April 2015 to May 2017)—showing significantly higher and lower proportions of cases than the rest of the UK, respectively, for the indicated time periods. A moderately significant high-risk cluster (P=0.087) was also identified in the Manchester area of northern England between February and April 2014. Habitat was the predictor with the highest relative contribution to CRGV distribution (20.3 per cent). Cases were generally associated with woodlands, increasing mean maximum temperatures in winter, spring and autumn, increasing mean rainfall in winter and spring and decreasing cattle and sheep density. Understanding of such factors may help develop causal models for CRGV occurrence.

scholarly journals A global bibliometric perspective on soil erosion modelling

2020 ◽  
Author(s):  
Nejc Bezak ◽  
Keyword(s):  
Conference Proceedings ◽  
Soil Erosion ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Field Activity ◽  
Erosion Modelling ◽  
Meta Information ◽  
Number Of Citations ◽  
Regression Tree Model

<p>Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.</p><p>As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries.  </p><p>Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.    </p>

scholarly journals Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

2019 ◽  
Vol 11 (23) ◽  
pp. 2756 ◽  
Author(s):  
Milewski ◽  
Thomas ◽  
Seyoum ◽  
Rasmussen
Keyword(s):  
Groundwater Level ◽  
Groundwater Resources ◽  
Large Body ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Groundwater Levels ◽  
Floridan Aquifer ◽  
Upper Floridan Aquifer ◽  
Spatial Downscaling

Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1°) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002–2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems.

scholarly journals Case Fatality Ratio Estimates for the 2013–2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation

2019 ◽  
Vol 70 (12) ◽  
pp. 2476-2483 ◽  
Author(s):  
Alpha Forna ◽  
Pierre Nouvellet ◽  
Ilaria Dorigatti ◽  
Christl A Donnelly
Keyword(s):  
Characteristic Curve ◽  
Regression Tree ◽  
Case Fatality ◽  
West African ◽  
Boosted Regression Trees ◽  
Survival Outcomes ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Case Fatality Ratio ◽  
Out Of Sample

Abstract Background The 2013–2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported. Methods Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. Results The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5–75.6%); specificity, 69.8% (54.1–75.6%); percentage correctly classified, 69.9% (53.7–75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8–82.1%). The adjusted CFR estimates (95% CI) for the 2013–2016 West African epidemic were 82.8% (45.6–85.6%) overall and 89.1% (40.8–91.6%), 65.6% (61.3–69.6%), and 79.2% (45.4–84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR. Conclusions The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.

The relationship between meteorological factors and mumps based on Boosted regression tree model

2019 ◽  
Vol 695 ◽  
pp. 133758 ◽  
Author(s):  
Dandan Zhang ◽  
Yuming Guo ◽  
Shannon Rutherford ◽  
Chang Qi ◽  
Xu Wang ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
The Relationship ◽  
Regression Tree Model

scholarly journals Risk mapping of scrub typhus infections in Qingdao city, China

2020 ◽  
Vol 14 (12) ◽  
pp. e0008757
Author(s):  
Hualei Xin ◽  
Peng Fu ◽  
Junling Sun ◽  
Shengjie Lai ◽  
Wenbiao Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Scrub Typhus ◽  
Control Strategies ◽  
Regression Tree ◽  
Infection Risk ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Risk Areas ◽  
Qingdao City ◽  
Favourable Environment

Background The emergence and re-emergence of scrub typhus has been reported in the past decade in many global regions. In this study, we aim to identify potential scrub typhus infection risk zones with high spatial resolution in Qingdao city, in which scrub typhus is endemic, to guide local prevention and control strategies. Methodology/Principal findings Scrub typhus cases in Qingdao city during 2006–2018 were retrieved from the Chinese National Infectious Diseases Reporting System. We divided Qingdao city into 1,101 gridded squares and classified them into two categories: areas with and without recorded scrub typhus cases. A boosted regression tree model was used to explore environmental and socioeconomic covariates associated with scrub typhus occurrence and predict the risk of scrub typhus infection across the whole area of Qingdao city. A total of 989 scrub typhus cases were reported in Qingdao from 2006–2018, with most cases located in rural and suburban areas. The predicted risk map generated by the boosted regression tree models indicated that the highest infection risk areas were mainly concentrated in the mid-east and northeast regions of Qingdao, with gross domestic product (20.9%±1.8% standard error) and annual cumulative precipitation (20.3%±1.1%) contributing the most to the variation in the models. By using a threshold environmental suitability value of 0.26, we identified 757 squares (68.7% of the total) with a favourable environment for scrub typhus infection; 66.2% (501/757) of the squares had not yet recorded cases. It is estimated that 6.32 million people (72.5% of the total population) reside in areas with a high risk of scrub typhus infection. Conclusions/Significance Many locations in Qingdao city with no recorded scrub typhus cases were identified as being at risk for scrub typhus occurrence. In these at-risk areas, awareness and capacity for case diagnosis and treatment should be enhanced in the local medical service institutes.

scholarly journals Spatial distribution of soil organic carbon stocks in France

2010 ◽  
Vol 7 (6) ◽  
pp. 8409-8443 ◽  
Author(s):  
M. P. Martin ◽  
M. Wattenbach ◽  
P. Smith ◽  
J. Meersmans ◽  
C. Jolivet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Regression Tree ◽  
Climatic Variables ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Regression Tree Model

Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, whereby it can influence the course of climate change. Changes in soil organic soil stocks (SOCS) are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOCS is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing circa 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOCS as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOCS for the whole of metropolitan France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on soil organic carbon for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOCS and pedo-climatic variables (plus their interactions) over the French territory. These relationship strongly depended on the land use, and more specifically differed between forest soils and cultivated soil. The total estimate of SOCS in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOCS distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOCS.

scholarly journals Spatial distribution of soil organic carbon stocks in France

2011 ◽  
Vol 8 (5) ◽  
pp. 1053-1065 ◽  
Author(s):  
M. P. Martin ◽  
M. Wattenbach ◽  
P. Smith ◽  
J. Meersmans ◽  
C. Jolivet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Regression Tree Model ◽  
Soc Stocks

Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks.

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