Mortensenfitzpatrick7770

The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.Recent findings indicate that both disruptive Post-Traumatic Stress Disorder (PTSD) and healthy Post-Traumatic Growth (PTG) responses have some spatial distribution depending on where they are measured and the different degrees of exposure that people may have to a critical event (e.g., earthquake). Less is known about how these responses change as a function of space and time after these events. The objective of this study was to enter deeper into this relationship analysing how PTSD and PTG responses vary in their spatial distribution 6 and 7 years after an earthquake (such as the one that occurred on 27 February, 2010 in Cauquenes City, Chile). Spatial analyses based on Geographic Information Systems (GIS) were performed to detect global and local geographic clustering. Investigating 171 (2016) and 106 (2017) randomly selected adults from Cauquenes, we demonstrated that 7 years after the event only 4 variables were spatially clustered, i.e. personal mental strength, interpersonal relations, new possibilities and appreciation of life), all of them PTG dimensions; This result contrasted with the situation the previous year (2016), when 7 variables were clustered (total PTG, spiritual change, new possibilities, appreciation of life, PTSD symptoms, PTSD reactions and PTSD in total). The spatial identifications found could facilitate the comparison of mental health conditions in populations and the impact of recovery programmes in communities exposed to disasters.Leptospirosis is a serious bacterial infection that occurs worldwide, with fatality rate of up to 40% in the most severe cases. The number of cases peaks during the rainy season and may reach epidemic proportions in the event of flooding. It is possible that people living in areas affected by natural disasters are at greater risk of contracting the disease. The aim of this study was to identify clusters of relatively higher risk for leptospirosis occurrence, both in space and time, in six municipalities of Santa Catarina, Brazil, which had the highest incidence of the disease between 2000 and 2016, and to evaluate if these clusters coincide with the occurrence of natural disasters. The cases were geocoded with the geographic coordinates of patients' home addresses, and the analysis was performed using SaTScan software. The areas mapped as being at risk for hydrological and mass movements were compared with the locations of detected leptospirosis clusters. The disease was more common in men and in the age group from 15 to 69 years. In the scan statistics performed, only space-time showed significant results. Clusters were detected in all municipalities in 2008, when natural disasters preceded by heavy rainfall occurred. One of the municipalities also had clusters in 2011. In these clusters, most of the cases lived in urban areas and areas at risk for experiencing natural disasters. The interaction between time (time of disaster occurrence) and space (areas at risk of experiencing natural disasters) were the determining factors affecting cluster formation.This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013-2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, P less then 0.001; fixed-effect coefficient = -10.340, P=0.005) followed by rainfall (random-effect coefficient = 1.353, P less then 0.001; fixed-effect coefficient = 1.347, P less then 0.001) and NTL density (random-effect coefficient = -0.569, P=0.004; fixed-effect coefficient = -0.564, P=0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA's results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.Human Immunodeficiency Virus (HIV) infection still represents an important public health problem, because it involves clinical, epidemiological, social, economic and political issues. We analyzed the temporal and spatial pattern of the HIV incidence in an area of social inequality in northeast Brazil and its association with socioeconomic indicators. An ecological study was carried out with a focus on all HIV cases reported in Alagoas State, Northeast Brazil from 2007 to 2016 using its 102 municipalities as the units of our analysis. Data from the Brazilian information systems were used. Georeferenced data were analyzed using TerraView 4.2.2 software, QGis 2.18.2 and GeoDa 1.14.0. Time trend analyses were performed by the Joinpoint Regression software and the spatial analyses included the empirical Bayesian model and Moran autocorrelation. SP600125 research buy Spatial regression was used to determine the influence of space on HIV incidence rate and socioeconomic inequalities. There was an increasing trend of HIV rates, especially in the municipalities of the interior.