•Public transport vehicles could be hotspots for virus transmission during pandemics.•A binary logistic regression model was developed to explain mode choice during COVID-19.•Compared to males ...females are more likely to use public transport relative to solo modes.•People with higher trip frequency are more likely to use public transport relative to solo modes.•People with higher income levels tend to use public transport less often.•Age was not a significant predictor of mode choices.
A sharp decline in public transport use has been reported worldwide since the outbreak of the COVID-19 pandemic. As the virus spreads through close contacts, particularly in closed environments, public transport vehicles could be considered as hotspots for its transmission. However, public transport operations cannot be entirely stopped as many people in developing countries rely on them for their travel needs. This study aims to provide insights into people's travel mode choices during the COVID-19 pandemic. Data, i.e., 1,516 complete survey responses, were obtained through a questionnaire that was conducted in Lahore, Pakistan. A binary logistic model was developed using the collected data to model the likelihood of choosing solo or public transport modes during COVID-19. The results explained that the respondents preferred solo modes more than the public transport modes during the pandemic. Gender, income, education, profession, trip frequency, car ownership, motorbike ownership, and an underlying factor that was defined as “safety precautions” were found to be significant predictors of the public transport choice relative to solo modes. Females tend to choose public transport modes relative to solo modes as compared to males. Private vehicle (car or motorbike) owners were less likely to use public modes relative to solo modes when compared to those who do not own private vehicles. The outcomes of this study could be important for the government authorities, policymakers, and transport operators to understand the public transport use in developing countries during pandemics. Such information will be useful to devise regulations and preventive measures to control infectious diseases associated with public transport use, particularly in developing countries, where private transport options are limited.
The present study explores one of the biggest causes of the lack of organizational knowledge creation: knowledge hiding (KH). KH can be provoked by the deviant and detached behaviours of leaders ...and/or the motivations of employees. In this context, leaders assume a key role in reducing the effect of KH. Through the lens of transformational leadership (TL), a sample of 758 European SMEs with a total number of 2,232 employees operating in a knowledge-intensive sector is investigated. The scope is to evaluate the correlation between the three main characteristics of transformational leadership (i.e., trust, a collaborative environment, and the involvement of employees) and the phenomenon of KH through a logistic regression analysis. It emerges that TL can influence the organizational context and redefine the behaviours related to KH. In addition, empathic leadership can provide added value for companies since a collaborative environment and common objectives reduce the level of KH.
Credit risk is the risk that has the greatest opportunity to occur in banking. The number of bad loans will also affect bank performance. The banking sector needs to know whether a prospective ...creditor is classified as a risky person or not. The purpose of this study is to classify creditors and compare the classification results through logistic regression with the maximum likelihood model and the Boosting algorithm, especially the AdaBoost algorithm, and to select a model with the Boosting algorithm Credit Scoring aims to classify prospective creditor into two classes, namely good prospective creditor (Performing Loan) and bad prospective creditor (Non Performing Loan) based on certain characteristics. The method often used for classifying creditor is logistic regression, but this method is less robust and less accurate than data mining. Thus, there is a need for methods that provide greater accuracy. Among the methods that have been proposed is a method called Boosting, which operates sequentially by applying a classification algorithm to the reweighted version of the training data set. This study uses 5 datasets. The first dataset is secondary data originating from data on non-subsidized homeownership creditors of Bank X Malang City. While the other datasets are simulation data with many samples of 10, 500, and 1000. The results of this study indicate that ensemble boosting logistic regression is more suitable for describing binary response problems, especially creditor classification because it provides more accurate information. For high-dimensional data, which is represented by a sample size of 10, ensemble logistic regression is proven to be able to produce fairly accurate predictions with an accuracy rate of up to 80%, whereas in the logistic regression analysis the model raises N.A because many samples < many independent variables. The use of boosting is preferred because it focuses on problems that are misclassified and have a tendency to increase to higher accuracy.
Background
Preventive measures to decrease the frequency and intensity of anaphylactic events are essential to provide optimal care for allergic patients. Aggravating factors may trigger or increase ...the severity of anaphylaxis and therefore need to be recognized and avoided.
Objective
To identify and prioritize factors associated with an increased risk of developing severe anaphylaxis.
Methods
Data from the Anaphylaxis Registry (122 centers in 11 European countries) were used in logistic regression models considering existing severity grading systems, elicitors, and symptoms to identify the relative risk of factors on the severity of anaphylaxis.
Results
We identified higher age and concomitant mastocytosis (OR: 3.1, CI: 2.6‐3.7) as the most important predictors for an increased risk of severe anaphylaxis. Vigorous physical exercise (OR: 1.5, CI: 1.3‐1.7), male sex (OR: 1.2, CI: 1.1‐1.3), and psychological burden (OR: 1.4, CI: 1.2‐1.6) were more often associated with severe reactions. Additionally, intake of beta‐blockers (OR: 1.9, CI: 1.5‐2.2) and ACE‐I (OR: 1.28, CI: 1.05, 1.51) in temporal proximity to allergen exposition was identified as an important factor in logistic regression analysis.
Conclusion
Our data suggest it may be possible to identify patients who require intensified preventive measures due to their relatively higher risk for severe anaphylaxis by considering endogenous and exogenous factors.
To reduce the estimator's variance and prevent overfitting, regularization techniques have attracted great interest from the statistics and machine learning communities. Most existing regularized ...methods rely on the sparsity assumption that a model with fewer parameters predicts better than one with many parameters. This assumption works particularly well in high-dimensional problems. However, the sparsity assumption may not be necessary when the number of predictors is relatively small compared to the number of training instances. This paper argues that shrinking the coefficients towards a low-variance data-driven estimate could be a better regularization strategy for such situations. For low-dimensional classification problems, we propose a naïve Bayes regularized logistic regression (NBRLR) that shrinks the logistic regression coefficients toward the naïve Bayes estimate to provide a reduction in variance. Our approach is primarily motivated by the fact that naïve Bayes is functionally equivalent to logistic regression if naïve Bayes' conditional independence assumption holds. Under standard conditions, we prove the consistency of the NBRLR estimator. Extensive simulation and empirical experimental results show that NBRLR is a competitive alternative to various state-of-the-art classifiers, especially on low-dimensional datasets.
•Propose a novel regularization method for classification problems.•Provide theoretical results, including consistency of the proposed estimator.•Provide extensive simulation and empirical experimental results, which support the competence of the proposed estimator.
Student desertion is a phenomenon that has spread significantly in many higher education institutions in Ecuador. The objective of the research was to develop a predictive model of student dropout ...based on multiple binary logistic regression, with the purpose of detecting possible dropouts. The methodology used consists of three phases: Phase 1: Analysis of variables; Phase 2: Formulation of the mathematical model; and Phase 3: Evaluation. For the estimation of the coefficients of the model, the SPSS tool was obtained. After the creation of the predictive model, it was concluded that the most significant variables that contribute to the diagnosis of dropout are marital status, age, gender, Note2s, and Note1s. It is also evident that students have a higher risk of dropping out if they are married and lower risk if they are single or divorced. Finally it was concluded that gender is a factor that directly influences dropout; male students are more likely to drop out than females.
Keywords: logistic regression, predictive model, desertion.
Resumen
La deserción estudiantil es un fenómeno que se ha extendido significativamente en gran cantidad de instituciones educativas de nivel superior en el Ecuador. El objetivo de la investigación fue desarrollar un modelo predictivo de deserción estudiantil basado en la regresión logística binaria múltiple, con el propósito de detectar a posibles desertores. La metodología utilizada consta de tres fases: Fase1: Análisis de variables. Fase2: Formulación del modelo matemático. Fase3: Evaluación. Para la estimación de los coeficientes del modelo se utilizó la herramienta SPSS. Posterior a la creación del modelo predictivo se llegó a concluir que las variables más significativas que aportan al diagnóstico de la deserción son estado civil, edad, género Nota2s y Nota1s, además se evidencia que los estudiantes tienen mayor riesgo de deserción si están casados y menor riesgo si están solteros o divorciados, finalmente se concluye, que el género es un factor que influye directamente en la deserción, los estudiantes masculinos son más propensos a desertar que los femeninos.
Palabras Clave: regresión logística, modelo predictivo, deserción.
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•The analysis of pesticides in the supply cleaning water of wineries is mandatory.•There is no legal requirement for the specific pesticides to be analysed.•Risk assessment ...methodology was used to select the pesticides to be analysed.•A risk index based on ISO 31010 has been developed.•At least, 25 % of the total of pesticides used in Spanish DOCa’s should be analysed.
The use of water in wineries is widespread and reaches its peak during the harvest of the vineyard, when it is used to clean and disinfect barrels, tanks, pipes, tools, for pushing the product and dragging the grape juice, for preparing fermentations, and so on. In this sense, according to the European Directive 2020/2184, pesticides in supply water must be analysed to avoid possible contamination in wines. Due to the lack of data on the number of or the pesticides to be analysed in water as required by this Directive, the aim of this work was to select the pesticides to be analysed on the basis of a risk assessment methodology. Information on pesticide water solubility, pesticide water persistence and pesticide oral lethal dose from Pesticide Properties Database was used to build a logistic regression model to predict risk. The pesticides listed in the EURL database for grapes and the information from the pesticides used in the different Spanish Designations of Origin (DOCa) were applied to the logistic regression model. The results obtained showed that only the 25 % of the pesticides pose a high risk and that only the 5 % of these pesticides are used in the different DOCa studied.
Sustainable Development Goal-3, as defined by the United Nations, aims to ensure healthy lives, and promote well-being for all at all ages. In the context of Bangladesh, achieving the targets of ...SDG-3 is a critical priority for public health and development. This study presents a data-driven exploration of SDG-3 in Bangladesh, focusing on maternal and child health indicators. We utilized data from multiple Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2018 to provide a comprehensive analysis of health-related trends and determinants. We employed rigorous statistical techniques, including backward stepwise logistic regression and Pearson correlation coefficients, to uncover insights into the status of maternal and child health in Bangladesh. The prevalence of safe childbirth practices exhibited a gradual improvement from 2011 (32.5%) to 2018 (50.2%). Notably, women with higher levels of education demonstrated a significantly higher likelihood of safe childbirth practices (odd ratio = 1.73, 95% Confidence Interval: 1.19 – 1.97). Additionally, residing in urban regions (odd ratio = 1.07, 95% Confidence Interval: 0.96 – 1.39), having access to mass media (odd ratio = 1.34, 95% Confidence Interval: 0.98 – 1.41), receiving antenatal care (odd ratio = 1.62, 95% Confidence Interval: 1.16 – 1.92), being in the rich wealth index category (odd ratio = 1.13, 95% Confidence Interval: 0.92 – 1.39), and choosing to deliver outside the home (odd ratio = 1.48, 95% Confidence Interval: 1.11 – 1.86) were all associated with a higher likelihood of safe childbirth practices. The findings from this study show the power of data-driven decision-making in shaping health policies and interventions. They reveal critical factors affecting health outcomes, offering a roadmap for policymakers and stakeholders to design evidence-based strategies for improving maternal and child health in Bangladesh. This proposed work not only contributes to the academic understanding of public health in Bangladesh but also serves as a practical guide for those working to achieve SDG-3. As Bangladesh continues its journey toward health and well-being for all, this study sets a benchmark for evidence-based policymaking, emphasizing the importance of data-driven perspectives in the pursuit of SDG.
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