scholarly journals Application of network analysis and cluster analysis for better prevention and control of swine diseases in Argentina

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234489
Author(s):  
Jerome N. Baron ◽  
Maria N. Aznar ◽  
Mariela Monterubbianesi ◽  
Beatriz Martínez-López
Keyword(s):  
Cluster Analysis ◽  
Network Analysis ◽  
Prevention And Control ◽  
And Cluster Analysis ◽  
Swine Diseases ◽  
And Control

scholarly journals Variability of serum IgG sialylation and galactosylation degree in women with advanced endometriosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katarzyna Sołkiewicz ◽  
Hubert Krotkiewski ◽  
Marcin Jędryka ◽  
Ewa M. Kratz
Keyword(s):  
Cluster Analysis ◽  
Sialic Acid ◽  
Control Group ◽  
Clinical Tool ◽  
Non Invasive ◽  
Healthy Women ◽  
Serum Igg ◽  
And Cluster Analysis ◽  
Invasive Diagnostics ◽  
And Control

AbstractEndometriosis is an inflammatory disease which diagnostics is difficult and often invasive, therefore non-invasive diagnostics methods and parameters are needed for endometriosis detection. The aim of our study was to analyse the glycosylation of native serum IgG and IgG isolated from sera of women classified as: with endometriosis, without endometriosis but with some benign ginecological disease, and control group of healthy women, in context of its utility for differentiation of advanced endometriosis from the group of healthy women. IgG sialylation and galactosylation/agalactosylation degree was determined using specific lectins: MAA and SNA detecting sialic acid α2,3- and α2,6-linked, respectively, RCA-I and GSL-II specific to terminal Gal and terminal GlcNAc, respectively. The results of ROC and cluster analysis showed that the serum IgG MAA-reactivity, sialylation and agalactosylation factor may be used as supplementary parameters for endometriosis diagnostics and could be taken into account as a useful clinical tool to elucidate women with high risk of endometriosis development. Additionally, we have shown that the analysis of native serum IgG glycosylation, without the prior time-consuming and expensive isolation of the protein, is sufficient to differentiation endometriosis from a group of healthy women.

Cluster analysis in the control over the activity of insurance companies

2020 ◽  
Vol 19 (3) ◽  
pp. 541-563 ◽  
Author(s):  
A.A. Turgaeva
Keyword(s):  
Cluster Analysis ◽  
Control System ◽  
Internal Control ◽  
Insurance Companies ◽  
Data Sampling ◽  
Mathematical Methods ◽  
Statistical Research ◽  
Internal Control System ◽  
And Cluster Analysis ◽  
And Control

Subject. The article considers clustering of insurance companies as a type of informatization of economy for practical application by the internal control system. Objectives. The purpose is to present clusters and give their interpretation for insurance companies in relation to internal control; identify the possibility of clustering, using the Deductor Studio platform developed by Base Group for internal control systems. Methods. The study employs techniques of statistical research and data processing, mathematical methods, methods of grouping, and cluster analysis. Results. Clusters are presented by several indicators of insurance companies. The study reveals heterogeneity in the results of distribution according to the rating of companies in terms of various indicators. It confirms the need to use the cluster analysis in the internal control system. Conclusions. Cluster analysis enables the internal control system to take into account all data regardless of their amount, and avoid data sampling. It reduces the level of errors in the results of analysis and control.

Analysis of COVID-19 Prevention and Control Modes and Effects

2021 ◽  
Vol 16 (4) ◽  
pp. 628-665
Author(s):  
Jian Zhou ◽  
◽  
Xiaohui Lu ◽  
Liang Ye ◽  
Yu Shao ◽  
...  
Keyword(s):  
Active Treatment ◽  
Prevention And Control ◽  
Sub Saharan Africa ◽  
The Us ◽  
The World ◽  
Sub Saharan ◽  
And Cluster Analysis ◽  
And Control ◽  
First Contact ◽  
South Europe

This study evaluates COVID-19 prevention and control policies. Based on the simulation, we compare the effects of two major policies: contact restriction and active treatment. Through regression and cluster analysis, we classified 169 countries and regions in the world into 10 groups, among which five groups accounted for the major proportion: the ones with the labels “CHN (China) mode,” “SE (South Europe) mode,” “ENE-SSA (East & North Europe and Sub-Saharan Africa) mode,” “US (United States) mode,” and “DEU (Germany) mode”). Differences in the effects of the prevention and control of COVID-19 in typical countries in each mode are comprehensively investigated. The conclusions of this study can be summarized as follows: First, contact restriction outperforms active treatment in curbing the spread of COVID-19. Second, “CHN mode” ranks the highest level of epidemic control and emphasizes epidemic prevention and control more than economic stimulus, which is the opposite of the “US mode”. Regression analysis reveals that the differences in epidemics worldwide are caused by policy differences among modes.

scholarly journals Epidemiological characteristics of 1212 COVID-19 patients in Henan, China

2020 ◽  
Author(s):  
Pei Wang ◽  
Jun-an Lu ◽  
Yanyu Jin ◽  
Mengfan Zhu ◽  
Lingling Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Network Analysis ◽  
Prevention And Control ◽  
Incubation Periods ◽  
Three Stages ◽  
And Control ◽  
Epidemiological Characteristics

AbstractBased on publicly released data for 1212 patients, we investigated the epidemiological characteristics of COVID-19 in Henan of China. The following findings are obtained: 1) COVID-19 patients in Henan show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences, possible causes were explored; 2) Statistical analysis on 483 patients reveals that the estimated average, mode and median incubation periods are 7.4, 4 and 7 days; Incubation periods of 92% patients were no more than 14 days; 3) The epidemic of COVID-19 in Henan has undergone three stages and showed high correlations with the numbers of patients that recently return from Wuhan; 4) Network analysis on the aggregate outbreak phenomena of COVID-19 revealed that 208 cases were clustering infected, and various people’s Hospital are the main force in treating patients. The related investigations have potential implications for the prevention and control of COVID-19.

Combining social network analysis and cluster analysis to identify sexual network types

2007 ◽  
Vol 18 (11) ◽  
pp. 754-759 ◽  
Author(s):  
E De Rubeis ◽  
J L Wylie ◽  
D W Cameron ◽  
R C Nair ◽  
A M Jolly
Keyword(s):  
Cluster Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sexual Network ◽  
And Cluster Analysis

scholarly journals Enhancing engagement beyond the conference walls: analysis of Twitter use at #ICPIC2019 infection prevention and control conference

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Romain Martischang ◽  
Ermira Tartari ◽  
Claire Kilpatrick ◽  
Graham Mackenzie ◽  
Vanessa Carter ◽  
...  
Keyword(s):  
Network Analysis ◽  
Prevention And Control ◽  
Healthcare Workers ◽  
Infection Prevention ◽  
Academic Research ◽  
Infection Prevention And Control ◽  
Academic Researchers ◽  
Scientific Congress ◽  
Twitter Users ◽  
And Control

Abstract Background Social media may provide a tool, when coupled with a patient-included™ conference, to enhance the engagement among the general public. We describe authors and potential readers of Twitter content surrounding a patient-included™ scientific congress, the International Consortium for Prevention and Infection Control (ICPIC) 2019. Methods Retrospective observational analysis of Twitter users posting with the #ICPIC2019 hashtag during the conference. Tweet authors, overall followers, and active followers were categorized according to their Twitter biographies using unsupervised learning. Diversity of professional backgrounds of Tweet authors and their followers was explored. Network analysis explored connectedness between the reach of authors. Results In total, 1264 participants attended ICPIC 2019, of which 28 were patients. From September 7 to 16, 2019, we were able to categorize 235′620 (41%) followers linked to 474 (76%) authors. Among authors and followers, respectively 34% and 14% were healthcare workers, 11% and 15% were from industry representatives, 8% and 7% were academic researchers. On average, 23% (range 9–39%) followers belonged to the same categories as authors. Among all followers categorized, only 582/235 620 (0.25%) interacted with original messages, including healthcare workers (37%), global and public health (12%), academic research (11%) and those from industry (11%). Though the similarity between Tweet authors and followers was supported by network analysis, we also observed that non-healthcare workers (including patients) appeared to have more diverse followers. Conclusions We observed the participation of numerous Tweet authors and followers from diverse professional backgrounds potentially supporting the benefit of including patients in conferences to reach a more general, non-specialized public.

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