Spatial disparities and associated factors of Under-five diarrhea disease in Ilubabor Zone, Oromia Regional State, Ethiopia

Diarrhea is commonly a sign of an infection in the intestinal tract that is caused by different bacteria, virus and parasitic entities. It is one of the leading causes of child mortality worldwide, especially in sub-Saharan Africa countries including Ethiopia. The main objective of this study was to identify spatial disparities and associated factors of under- five diarrhea disease in Ilubabor zone, Oromia regional state, Ethiopia. The study has been conducted in Ilu Aba Bor zone of entire districts and the data is basically both primary and secondary which were obtained from each woreda health office of Ilu Aba Bor zone and corresponding mother or care givers of sampled child. Spatial disparities of under-five diarrhea were identified using global and local measures of spatial autocorrelation. Geo-additive regression model was used to identify the spatial disparities and associated factors of under-five diarrheal disease. The value of global and local measures of spatial autocorrelation shows that under-five diarrheal disease varies according to geographical location and shows significant positive spatial autocorrelation. The results of Geo-additive regression model showed that statistically significant relationship between under-five diarrhea disease and independent variables .There is evidence of significant under-five diarrheal disease clustering in Ilu Aba Bor zone, southwest Ethiopia. Model based data analysis showed that there is significant relationship between Under-five diarrhea and covariates (mother’s age, mother’s education, source of drinking water, quality of toilet facility, DPT 3 vaccination, Polio 3 vaccination and household wealth index.).

Spatial clustering and contextual factors associated with hospitalisation and deaths due to COVID-19 in Sweden: a geospatial nationwide ecological study

IntroductionIn Sweden, thousands of hospitalisations and deaths due to COVID-19 were reported since the pandemic started. Considering the uneven spatial distribution of those severe outcomes at the municipality level, the objective of this study was, first, to identify high-risk areas for COVID-19 hospitalisations and deaths, and second, to determine the associated contextual factors with the uneven spatial distribution of both study outcomes in Sweden.MethodsThe existences of spatial autocorrelation of the standardised incidence (hospitalisations) ratio and standardised mortality ratio were investigated using Global Moran’s I test. Furthermore, we applied the retrospective Poisson spatial scan statistics to identify high-risk spatial clusters. The association between the contextual demographic and socioeconomic factors and the number of hospitalisations and deaths was estimated using a quasi-Poisson generalised additive regression model.ResultsTen high-risk spatial clusters of hospitalisations and six high-risk clusters of mortality were identified in Sweden from February 2020 to October 2020. The hospitalisations and deaths were associated with three contextual variables in a multivariate model: population density (inhabitants/km2) and the proportion of immigrants (%) showed a positive association with both outcomes, while the proportion of the population aged 65+ years (%) showed a negative association.ConclusionsOur study identified high-risk spatial clusters for hospitalisations and deaths due to COVID-19 and the association of population density, the proportion of immigrants and the proportion of people aged 65+ years with those severe outcomes. Results indicate where public health measures must be reinforced to improve sustained and future disease control and optimise the distribution of resources.

Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline

Extensive, but remote oil and gas fields of the United States, Canada, and Russia require the construction and operation of extremely long pipelines. Global warming and local heating effects lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this causes changes in the spatial positions and forms of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the remoted pipelines in order to be able to perform remedial measures in time. They are therefore seeking methods for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting uncorrelated time series; generating synthetic features; robust scaling temperature series, tuning the additive regression model to forecast soil temperatures.

Detecting Anomalies in Sequences of Short Text Using Iterative Language Models

Business managers using Intelligent Virtual Assistants (IVAs) to enhance their company's customer service need ways to accurately and efficiently detect anomalies in conversations between the IVA and customers, vital for customer retention and satisfaction. Unfortunately, anomaly detection is a challenging problem because of the subjective nature of what is defined as anomalous. Detecting anomalies in sequences of short texts, common in chat settings, is even more difficult because independently generated texts are similar only at a semantic level, resulting in an abundance of false positives. In addition, literature for detecting anomalies in time ordered sequences of short text is shallow considering the abundance of such data sets in online settings. We introduce a technique for detecting anomalies in sequences of short textual data by adaptively and iteratively learning low perplexity language models. Our algorithm defines a short textual item as anomalous when its cross-entropy exceeds the upper confidence interval of a trained additive regression model. We demonstrate successful case studies and bridge the gap between theory and practice by finding anomalies in sequences of real conversations with virtual chat agents. Empirical evaluation shows that our method achieves, on average, 31% higher max F1 scores than the baseline method of non-negative matrix factorization across three large human-annotated sequences of short texts.

Topographic Kernels for Gaussian Process Regression in Digital Soil Mapping

<p>Gaussian Processes provide a theoretically well-understood regression framework that is widely used in the context of Digital Soil Mapping. Among the reasons to use Gaussian Process Regression (GPR) are its interpretability, its builtin support for uncertainty quantification, and its ability to handle unevenly spaced and correlated training samples through a user-specified covariance kernel. The base case of GPR is performed with covariance models that are specified functions of Euclidean distance. In order to incorporate information other than the relative positions, regression-kriging extends GPR by an additive regression model of choice, and co-kriging considers a covariance model between covariates and the target variable. In this work, we use the alternative approach of incorporating topographic information directly into the kernel function by use of a non-Euclidean, non-stationary distance function. In particular, we devise kernels based on a path of least effort, where <em>effort</em> is locally specified as a function constructed from prior knowledge. It can e.g. be derived from local topographic variables. We demonstrate that our candidate models improve prediction accuracy over the base model. This shows that domain knowledge can be integrated into the model by means of handcrafted kernel functions. The approach is not per se restricted to topographic variables, but could be used for any covariate quantity that is available at output resolution.</p>

Genetic Variants rs1049255 CYBA and rs2333227 MPO are Associated with Susceptibility to Coronary Artery Disease in Russian Residents of Central Russia

Aim To study association of single-nucleotide polymorphisms rs1049255 CYBA and rs2333227 MPO with development of ischemic heart disease (IHD) in Russian residents of Central Russia.Material and methods The study material was DNA samples from 436 patients with IHD (265 men, 171 women; mean age, 61 years) and 370 sex- and age-matched arbitrarily healthy volunteers (209 men, 161 women; mean age, 60 years). Genotyping was performed by allelic discrimination with TaqMan probes.Results Comparative analysis of genotype frequency (log-additive regression model) showed that SNP rs1049255 CYBA (odds ratio, OR, 0.79 at 95 % confidence interval, CI, from 0.65 to 0.96; p=0.02) and rs2333227 MPO (OR 0.72 at 95 % CI from 0.55 to 0.95; p=0.02) were associated with a decreased risk of IHD adjusted for sex and age. Analysis of sex-specific effects showed that the protective effect of rs1049255 CYBA was evident only in men (OR 0.72 at 95 % CI from 0.55 to 0.94; p=0.16).Conclusion The study demonstrated a protective effect of rs1049255 CYBA and rs2333227 MPO with respect of IHD in Russians. The protective effect of rs1049255 CYBA was observed only in men.