The Analysis of Influencing Factors on the Number of Storms

2012 ◽  
Vol 166-169 ◽  
pp. 2649-2653
Author(s):  
Bin Hui Wang ◽  
Zhi Jian Wang ◽  
Si Ling Chen
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Poisson Regression ◽  
West African ◽  
The Other ◽  
Poisson Regression Model ◽  
Probability Of Occurrence ◽  
Wet Weather ◽  
The Impact ◽  
Non Parametric

By using both parametric and non-parametric tests, noticed that both EI Nino and West African Wetness have significant impact on the number of storms. A Poisson Regression Model is then be used to further explores the impact of different variables to the number of storms. In particular, warm phase of EI Nino and dry weather has suppress impact on the number of storms while cold phase of Nino and wet weather encourage storms. Under the combination impact of both EI Nino and West African Wetness, the probability of occurrence of extreme storms is higher than under the other conditions.

Both microbiological surveillance and audit of procedures improve reprocessing of flexible bronchoscopes and patient safety

2021 ◽  
pp. 1-7
Author(s):  
Philippe Saliou ◽  
Lila Calmettes ◽  
Hervé Le Bars ◽  
Christopher Payan ◽  
Valérie Narbonne ◽  
...  
Keyword(s):  
Risk Factors ◽  
Quality Control ◽  
Regression Model ◽  
Poisson Regression ◽  
Control Measure ◽  
Poisson Regression Model ◽  
Microbiological Contamination ◽  
Quality Control Measure ◽  
The Impact ◽  
Microbiological Surveillance

Abstract Background: Microbiological surveillance of bronchoscopes and automatic endoscope reprocessors (AERs)/washer disinfectors as a quality control measure is controversial. Experts also are divided on the infection risks associated with bronchoscopic procedures. Objective: We evaluated the impact of routine microbiological surveillance and audits of cleaning/disinfection practices on contamination rates of reprocessed bronchoscopes. Design: Audits were conducted of reprocessing procedures and microbiological surveillance on all flexible bronchoscopes used from January 2007 to June 2020 at a teaching hospital in France. Contamination rates per year were calculated and analyzed using a Poisson regression model. The risk factors for microbiological contamination were analyzed using a multivariable logistical regression model. Results: In total, 478 microbiological tests were conducted on 91 different bronchoscopes and 57 on AERs. The rate of bronchoscope contamination significantly decreased between 2007 and 2020, varying from 30.2 to 0% (P < .0001). Multivariate analysis confirmed that retesting after a previous contaminated test was significantly associated with higher risk of bronchoscope contamination (OR, 2.58; P = .015). This finding was explained by the persistence of microorganisms in bronchoscopes despite repeated disinfections. However, the risk of persistent contamination was not associated with the age of the bronchoscope. Conclusions: Our results confirm that bronchoscopes can remain contaminated despite repeated reprocessing. Routine microbial testing of bronchoscopes for quality assurance and audit of decontamination and disinfection procedures can improve the reprocessing of bronchoscopes and minimize the rate of persistent contamination.

scholarly journals PEMODELAN JUMLAH ANAK PUTUS SEKOLAH DI PROVINSI BALI DENGAN PENDEKATAN SEMI-PARAMETRIC GEOGRAPHICALLY WEIGHTED POISSON REGRESSION

2013 ◽  
Vol 2 (3) ◽  
pp. 29
Author(s):  
GUSTI AYU RATIH ASTARI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
High School ◽  
Regression Model ◽  
Influencing Factors ◽  
Primary Education ◽  
Junior High School ◽  
Poisson Regression ◽  
Junior High ◽  
Family Members ◽  
Education Sector ◽  
Poisson Regression Model

Dropout number is one of the important indicators to measure the human progress resources in education sector. This research uses the approaches of Semi-parametric Geographically Weighted Poisson Regression to get the best model and to determine the influencing factors of dropout number for primary education in Bali. The analysis results show that there are no significant differences between the Poisson regression model with GWPR and Semi-parametric GWPR. Factors which significantly influence the dropout number for primary education in Bali are the ratio of students to school, ratio of students to teachers, the number of families with the latest educational fathers is elementary or junior high school, illiteracy rates, and the average number of family members.

scholarly journals Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Tayeb Mohammadi ◽  
Soleiman Kheiri ◽  
Morteza Sedehi
Keyword(s):  
Blood Transfusion ◽  
Regression Model ◽  
Bayesian Approach ◽  
Poisson Regression ◽  
Blood Donation ◽  
Poisson Model ◽  
Joint Modeling ◽  
Information Criteria ◽  
The Other ◽  
Poisson Regression Model

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

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