scholarly journals Unsupervised Clustering in Epidemiological Factor Analysis

2021 ◽  
Vol 14 (1) ◽  
pp. 63-72
Author(s):  
Serge Dolgikh
Keyword(s):  
Principal Component ◽  
Epidemiological Data ◽  
Early Analysis ◽  
Contributing Factors ◽  
Machine Learning Methods ◽  
Preventative Measures ◽  
Principal Factors ◽  
Local Exposure ◽  
Factors Of Influence ◽  
Epidemiological Situation

Background: The analysis of epidemiological data at an early phase of an epidemiological situation, when the confident correlation of contributing factors to the outcome has not yet been established, may present a challenge for conventional methods of data analysis. Objective: This study aimed to develop approaches for the early analysis of epidemiological data that can be effective in the areas with less labeled data. Methods: An analysis of a combined dataset of epidemiological statistics of national and subnational jurisdictions, aligned at approximately two months after the first local exposure to COVID-19 with unsupervised machine learning methods, including principal component analysis and deep neural network dimensionality reduction, to identify the principal factors of influence was performed. Results: The approach and methods utilized in the study allow to clearly separate milder background cases from those with the most rapid and aggressive onset of the epidemics. Conclusion: The findings can be used in the evaluation of possible epidemiological scenarios and as an effective modeling approach to identify possible negative epidemiological scenarios and design corrective and preventative measures to avoid the development of epidemiological situations with potentially severe impacts.

Identifying Explosive Epidemiological Cases with Unsupervised Machine Learning (Preprint)

2020 ◽  
Author(s):  
Serge Dolgikh
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Local Time ◽  
Principal Component ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Modeling Tool ◽  
Machine Learning Methods ◽  
Preventative Measures

UNSTRUCTURED An analysis of a combined dataset of Wave 1 and 2 cases, aligned at approximately Local Time Zero + 2 months with unsupervised machine learning methods such as Principal Component Analysis and deep autoencoder dimensionality reduction allows to clearly separate milder background cases from those with more rapid and aggressive onset of the epidemics. The analysis and findings of the study can be used in evaluation of possible epidemiological scenarios and as an effective modeling tool to design corrective and preventative measures to avoid developments with potentially heavy impact.

scholarly journals Identifying Explosive Epidemiological Cases with Unsupervised Machine Learning

2020 ◽  
Author(s):  
Serge Dolgikh
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Modeling Tool ◽  
Machine Learning Methods ◽  
Preventative Measures ◽  
Local Exposure

AbstractAn analysis of a combined dataset of epidemiological statistics of national and subnational jurisdictions, aligned at approximately two months after the first local exposure to Covid-19 with unsupervised machine learning methods such as PCA and deep autoencoder dimensionality reduction allows to clearly separate milder background cases from those with more rapid and aggressive onset of the epidemics. The analysis and findings of this study can be used in evaluation of possible epidemiological scenarios and as an effective modeling tool to design corrective and preventative measures to avoid developments with potentially heavy impact

scholarly journals Brucellosis morbidity in Russia and Primorsky region

2022 ◽  
pp. 50-55
Author(s):  
G. T. Dzyuba ◽  
Y. E. Skurikhina ◽  
G. A. Zakharova ◽  
A. V. Ponomareva
Keyword(s):  
Average Rate ◽  
Federal District ◽  
Epidemiological Data ◽  
Morbidity Rate ◽  
Disease Rate ◽  
Occupational Groups ◽  
Current State ◽  
Far Eastern ◽  
Epidemiological Situation

Objective: Epidemiological data analysis concerning the spread of brucellosis in Russia and Primorsky Region, study of the manifestations of the epidemic process and the current state of brucellosis issue.Methods: Descriptive epidemiological analysis of materials on infectious morbidity based on state statistical reporting forms No. 1, No. 2 “Information on infectious and parasitic morbidity”.Results: Brucellosis affects workers in the main occupational groups of livestock breeders, workers involved in the processing of livestock, who contact sick animals and other sources of brucellosis infection. Primorsky Region is a territory with an unstable epidemiological situation on brucellosis. A different morbidity rates were registered over the years. However usually it does not exceed the national average rate. In 2009, the morbidity rate counted 27.5% less than in Russian Federation (0.21 and 0.29 per 100,000 respectively), compared to previous years, the average long-term morbidity rate in the region increased by 3.5 times. The disease rate for the period from 2009 to 2019 ranged from 0.05 to 0.21%. In 2012, it exceeded the index in the Far Eastern Federal District (0.1 per 100,000 population). Brucellosis cases were detected among livestock breeders during an extraordinary medical examination due to the bad situation on brucellosis among animals.Conclusions: In recent decades, the epizootic and epidemiological situation on brucellosis remains tense due to the decrease in the number of serological and bacteriological studies among animals and humans, the weakening of veterinary-sanitary control and the formation of new private farms.

scholarly journals Student Satisfaction: Importance of Civility and University Identification Predictors

2021 ◽  
Vol 5 (4) ◽  
pp. 1
Author(s):  
LaVelle Hendricks ◽  
Stephen Reysen ◽  
Zaidy Mohdzain ◽  
Kelly Martin ◽  
Joy Teles Oliveira ◽  
...  
Keyword(s):  
Student Satisfaction ◽  
Undergraduate Students ◽  
Contributing Factors ◽  
Academic Context ◽  
University Environment ◽  
University Campuses ◽  
Preventative Measures ◽  
Uncivil Behavior ◽  
Teaching Learning ◽  
The University

Uncivil behavior has recently been a topic of concern within university campuses. Administration, faculty, and students are all subject to engage or experience uncivil conduct. Academic civility is a fundamental variable to establish a positive university environment and teaching-learning outcomes. Objective: The present paper analyzes the concept of civility and incivility within an academic context, offers a dimension perspective of the problem, reviews contributing factors associated with incivility, explores preventative measures for incivility, investigates academic strategies that promote civility, and makes recommendations for intervention strategies. A study that examines relationships between civility, university identification and satisfaction with the university was conducted. Methods: undergraduate students (N=588) from a state university answered a survey that evaluated three variables: civility, university identification, and university satisfaction. Results: Civility was positively related to university identification (r = .11, p = .009) and satisfaction (r = .16, p < .001), and identification was related to greater satisfaction with the university (r = .75, p < .001). Conclusion: Greater levels of civility are correlated with university identification and satisfaction. In order to improve teaching-learning environments, academic institutions are encouraged to promote an environment that is conducive to civility, as well as establish civil behaviors as part of their instruction.

scholarly journals Brucellosis and tuberculosis in cattle in South America

2018 ◽  
Vol 55 (2) ◽  
pp. e141139
Author(s):  
José Soares Ferreira Neto
Keyword(s):  
New Zealand ◽  
South America ◽  
Epidemiological Data ◽  
Review Article ◽  
South American ◽  
Cattle Population ◽  
High Quality ◽  
The World ◽  
Meat Market ◽  
Epidemiological Situation

In general, European and North American countries, as well as Australia and New Zealand, have already eradicated or reached good levels of control of brucellosis and tuberculosis in cattle. In the rest of the world, however, the epidemiological situation of these two diseases is frequently poorly understood. In this review article, quantified data on these diseases in the South American countries are presented. Initially, the aspects that led the continent to host 25% of the world cattle population are presented, in addition to the aspects that placed the continent at a prominent position in the international meat market. Subsequently the continent was divided into three country groups, considering the size of the cattle population and how well the epidemiological situation of brucellosis and tuberculosis in cattle is quantified. It is argued that countries that do not generate high-quality quantitative epidemiological data on these diseases have serious limitations in outlining and managing control or eradication strategies. Thus, for successful outcomes, at least methodologies to estimate the prevalence of infected herds should be employed.

Vegetation of Beni Bousera (northern Morocco) ultramafic soils and adjacent non-ultramafic soils in relation to edaphic factors

2015 ◽  
Vol 63 (4) ◽  
pp. 353
Author(s):  
A. El Ghalabzouri ◽  
R. Ajbilou ◽  
M. G. Mariotti ◽  
K. Targuisti ◽  
M. Ater
Keyword(s):  
Species Composition ◽  
Principal Component ◽  
Physicochemical Characteristics ◽  
Exchange Capacity ◽  
Ultramafic Soils ◽  
Significant Difference ◽  
Principal Factors ◽  
Calcium Magnesium ◽  
Clear Differentiation ◽  
Student’S T

Vegetation and soil sampling were undertaken in 47 relevés in two different sites in Beni Bousera (northern Morocco), 32 relevés were on ultramafic sites (peridotite) and 15 were on adjacent non-ultramafic soils (mica-schist). Soil composition of exchangeable elements (nickel, calcium, magnesium, calcium : magnesium ratio, iron, copper) and physicochemical characteristics (e.g. pH, cation exchange capacity, conductivity, carbonate) were investigated and the species composition and cover were recorded. Although there was a significant difference between the two types of soil shown by Student’s t-test and principal component analysis, the ‘serpentine factor’ can be considered moderate on the basis of the observed heavy metal concentrations and of the related calcium : magnesium ratio. Correspondence and canonical analysis were used to detect the principal factors associated with gradients in species composition. This analysis showed a clear differentiation of vegetation among the plant communities. Preferential or strict serpentinophyte species were recognised, whereas no hyperaccumulator species have yet been found.

Tropical principal component analysis on the space of phylogenetic trees

2020 ◽  
Vol 36 (17) ◽  
pp. 4590-4598
Author(s):  
Robert Page ◽  
Ruriko Yoshida ◽  
Leon Zhang
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Phylogenetic Trees ◽  
Principal Component ◽  
Component Analysis ◽  
Fixed Number ◽  
Supplementary Information ◽  
Gene Trees ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Motivation Due to new technology for efficiently generating genome data, machine learning methods are urgently needed to analyze large sets of gene trees over the space of phylogenetic trees. However, the space of phylogenetic trees is not Euclidean, so ordinary machine learning methods cannot be directly applied. In 2019, Yoshida et al. introduced the notion of tropical principal component analysis (PCA), a statistical method for visualization and dimensionality reduction using a tropical polytope with a fixed number of vertices that minimizes the sum of tropical distances between each data point and its tropical projection. However, their work focused on the tropical projective space rather than the space of phylogenetic trees. We focus here on tropical PCA for dimension reduction and visualization over the space of phylogenetic trees. Results Our main results are 2-fold: (i) theoretical interpretations of the tropical principal components over the space of phylogenetic trees, namely, the existence of a tropical cell decomposition into regions of fixed tree topology; and (ii) the development of a stochastic optimization method to estimate tropical PCs over the space of phylogenetic trees using a Markov Chain Monte Carlo approach. This method performs well with simulation studies, and it is applied to three empirical datasets: Apicomplexa and African coelacanth genomes as well as sequences of hemagglutinin for influenza from New York. Availability and implementation Dataset: http://polytopes.net/Data.tar.gz. Code: http://polytopes.net/tropica_MCMC_codes.tar.gz. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hydrochemical and multivariate analysis of groundwater quality in the northwest of Sinai, Egypt

2017 ◽  
Vol 15 (4) ◽  
pp. 644-657 ◽  
Author(s):  
M. F. El-Shahat ◽  
M. A. Sadek ◽  
W. M. Salem ◽  
A. A. Embaby ◽  
F. A. Mohamed
Keyword(s):  
Groundwater Quality ◽  
Groundwater Resources ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Human Consumption ◽  
Development Programs ◽  
Aquifer Recharge ◽  
Economic Activities ◽  
Groundwater Samples ◽  
Principal Factors

The northwestern coast of Sinai is home to many economic activities and development programs, thus evaluation of the potentiality and vulnerability of water resources is important. The present work has been conducted on the groundwater resources of this area for describing the major features of groundwater quality and the principal factors that control salinity evolution. The major ionic content of 39 groundwater samples collected from the Quaternary aquifer shows high coefficients of variation reflecting asymmetry of aquifer recharge. The groundwater samples have been classified into four clusters (using hierarchical cluster analysis), these match the variety of total dissolvable solids, water types and ionic orders. The principal component analysis combined the ionic parameters of the studied groundwater samples into two principal components. The first represents about 56% of the whole sample variance reflecting a salinization due to evaporation, leaching, dissolution of marine salts and/or seawater intrusion. The second represents about 15.8% reflecting dilution with rain water and the El-Salam Canal. Most groundwater samples were not suitable for human consumption and about 41% are suitable for irrigation. However, all groundwater samples are suitable for cattle, about 69% and 15% are suitable for horses and poultry, respectively.

scholarly journals Analysis of 15 years of the National Program for the Control and Eradication of Animal Brucellosis and Tuberculosis, Brazil

2016 ◽  
Vol 37 (5Supl2) ◽  
pp. 3385 ◽  
Author(s):  
José Soares Ferreira Neto ◽  
Gabriela Bicca da Silveira ◽  
Barbara Medeiros Rosa ◽  
Vitor Salvador Picão Gonçalves ◽  
José Henrique Hildebrand Grisi-Filho ◽  
...  
Keyword(s):  
Major Part ◽  
Epidemiological Data ◽  
National Program ◽  
Bovine Brucellosis ◽  
Veterinary Services ◽  
High Quality ◽  
Mato Grosso ◽  
Mato Grosso Do Sul ◽  
Epidemiological Situation ◽  
Control And Eradication

In 2001, Brazil launched the National Program for Control and Eradication of Animal Brucellosis and Tuberculosis (PNCEBT). After 15 years, After 15 years, it can be checked that there was mistakes and successes in driving the program, but it is undeniable that in this period, a series of structuring actions was initiated. In addition, a large volume of high-quality epidemiological data were produced, which will allow the country to move forward more rationally and safely in combating these two diseases. Today, Brazil have a sufficient contingent of veterinarians to develop the accreditation of farms and vaccination against brucellosis in all States; all batches of vaccines against brucellosis produced by private laboratories are controlled by an official laboratory; the brucellosis vaccination program is well established in most States and it has produced a decrease in prevalence in Mato Grosso, Mato Grosso do Sul, Rondônia and Minas Gerais; there are two ongoing eradication experiences: of brucellosis in Santa Catarina and of tuberculosis in Mato Grosso; nowadays there is a culture to combat brucellosis and tuberculosis in the Brazilian Official Veterinary Services. The epidemiological situation of bovine brucellosis and tuberculosis is well known in the major part of the country. However, progress has been limited by the difficulty in engaging the beef and dairy productive chains as true partners in the process.

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