Modelagem estatística das internações hospitalares por pneumonia em Campo Grande

Justification and Objectives: Brazil lacks consistent epidemiological data on the respiratory morbidity of children and older adults, which makes it difficult to plan and execute effective preventive and health promotion actions. The objective of this study was to analyze the adjustments of distributions (Weibull, Normal, Gamma, Logistic) of historical series of hospitalizations for respiratory diseases (total hospitalizations), from 2011 to 2015, in Campo Grande, Mato Grosso do Sul. Methods: to determine the statistical models, four statistical indicators (coefficient of determination, mean root square error, mean absolute error and mean absolute percentage error) were performed from 2011 to 2015. Parameter estimates are obtained for the models adopted in the study, with and without a regression structure. Results: the results showed that Weibull, Gamma, Normal and Logistic distributions, applied to the series of hospitalizations for respiratory diseases in Campo Grande, were satisfactory in determining the shape and scale parameters, and the statistical indicators R2, MAE, RSME and MAPE confirmed the data goodness-of-fit, and the graphical analysis indicated a satisfactory distribution fit. Conclusion: the analysis of monthly values indicates that Gamma is the best of the four distributions based on those selected. The regression model can be adjusted to the data and used as an alternative distribution that describes the hospitalization data considered in Campo Grande, Brazil.Justificación y Objetivos: el Brasil carece de datos epidemiológicos consistentes sobre la morbilidad respiratoria de niños y ancianos, lo que dificulta la planificación y ejecución de acciones efectivas de prevención y promoción de la salud. El objetivo de este estudio fue analizar los ajustes de las distribuciones (Weibull, Normal, Gamma, Logística) de la serie histórica de hospitalizaciones por enfermedades respiratorias (hospitalizaciones totales), de 2011 a 2015, en Campo Grande, Mato Grosso do Sul. Métodos: para la determinación de los modelos estadísticos, se realizaron cuatro indicadores estadísticos (coeficiente de determinación, raíz del error cuadrático medio, error medio absoluto y error porcentual absoluto medio) de 2011 a 2015. Se obtienen estimaciones de los parámetros para los modelos adoptados en el estudio, con y sin estructura de regresión. Resultados: los resultados mostraron que las distribuciones Weibull, Gamma, Normal y Logística, aplicadas a la serie de internaciones por enfermedades respiratorias en Campo Grande, fueron satisfactorias en la determinación de los parámetros de forma y escala, y los indicadores estadísticos R2, MAE, RSME y MAPE confirmaron la calidad de ajuste de los datos, y el análisis gráfico indicaron un ajuste satisfactorio de las distribuciones. Conclusión: el análisis de los valores mensuales indica que la Gamma es la mejor de las cuatro distribuciones en base a las seleccionadas. El modelo de regresión se puede ajustar a los datos y utilizar como una distribución alternativa que describe los datos de hospitalización considerados en Campo Grande, Brasil.Justificativa e Objetivos: o Brasil carece de dados epidemiológicos consistentes sobre a morbidade respiratória de crianças e idosos, o que dificulta o planejamento e a execução de ações efetivas de prevenção e promoção da saúde. O objetivo deste estudo foi analisar os ajustes das distribuições (Weibull, Normal, Gamma, Logística) da série histórica de internações por doenças respiratórias (total de internações), no período de 2011 a 2015, em Campo Grande, Mato Grosso do Sul. Métodos: para determinar os modelos estatísticos, foram executados quatro indicadores estatísticos (coeficiente de determinação, erro quadrático médio, erro absoluto médio e erro percentual absoluto médio) de 2011 a 2015. As estimativas dos parâmetros são obtidas para os modelos adotados no estudo com e sem uma estrutura de regressão. Resultados: os resultados mostraram que as distribuições Weibull, Gamma, Normal e Logística, aplicadas à série de internações por doenças respiratórias em Campo Grande, foram satisfatórias na determinação dos parâmetros de forma e escala, e os indicadores estatísticos R2, MAE, RSME e MAPE confirmaram a qualidade do ajuste dos dados, e a análise gráfica apontou um ajuste satisfatório das distribuições. Conclusão: a análise dos valores mensais indica que a Gamma é a melhor das quatro distribuições baseadas nos selecionados. O modelo de regressão pode ser ajustado aos dados e ser usado como uma distribuição alternativa que descreve os dados de internação considerados em Campo Grande, Brasil

Better Confidence Intervals for a Binomial Proportion

Interval estimation of a binomial proportion is one of the basic problems in statistics. In technical practice a binomial proportion is often used in statistical quality control. The standard Wald interval and the exact Clopper-Pearson interval are the most common and frequently used intervals. They are presented in the majority of statistical literature. It is known that the Wald interval performs poorly and this interval should not be used. In this paper we recommend the alternatives of confidence intervals that have a better performance and are appropriate for practical use. We compare the performance of six alternatives of confidence intervals for a binomial proportion: the Wald interval, the Clopper-Pearson interval, the Wilson score interval, the Wilson score interval with continuity correction, the Agresti-Coull interval and the Jeffreys interval in terms of the coverage probability, the interval length and the root mean square error

The Least Square and the Weighted Least Square Methods for Estimating the Weibull Distribution Parameters - A Comparative Study

In this paper we study the performance of the least square method and the weighted least square method for estimating the Weibull distribution parameters. In engineering practice these methods are commonly used due to their simplicity. The estimates of the parameters can be calculated easily by the closed-form formula. We consider three estimators of the cumulative distribution function and the weight factor proposed by Bergman (1986). The methods are compared in terms of the root mean square error and sample size. The comparison is based on the Monte Carlo simulation. The comparison shows that the weight factor improves the accuracy of the estimation the Weibull distribution parameters

Statistical Analysis of Wind Speed Data Based on Weibull and Rayleigh Distribution

In this paper the wind speed data from Meteorological observatory Bratislava-Mlynska dolina were statistically analyzed. The data were analyzed based on Weibull and Rayleigh distribution. The distribution parameters were estimated using maximum likelihood method. The both distributions were compared for their performance using the coefficient of determination and the root mean square error. The results indicate that the better performance can be obtained by the Weibull distribution

Monte Carlo Comparison of the Methods for Estimating the Weibull Distribution Parameters - Wind Speed Application

Wind energy is considered one of the most significant alternative energy sources for electrical energy production. The most important factor for modelling wind energy is the wind speed. The two-parameter Weibull distribution is commonly used for modelling wind speed. Its parameters play an important role in the wind energy applications and so it is important to choose the best method for the estimation of these parameters. In this paper we investigate and compare six different methods for estimating the Weibull distribution parameters. The methods we considered are the maximum likelihood method, the method of moments, the empirical method, the power density method, the least squares method and the weighted least squares method. The performance of these methods is compared and discussed through the Monte Carlo simulation

Sensitivity of Cell Cultures on Time-Varying Low-Frequency Magnetic Field Changes

This article is focused on the in vitro experimental verification of the basic necessary conditions for valid interpretation of an ion parametric resonance prediction model of biological reaction on an externally applied, extremely low-frequency magnetic field. Experiments are performed on model organism Saccharomyces cerevisiae strain BY4741, with the intention to target calcium ions, which already produced interesting preliminary results within authors’ previous works, confirming specific magnetic field settings being bioactive. To study the nature and origin of biological reactions more exhaustingly, the research is aimed at two chosen physical aspects of ion parametric resonance theory. In the first part, experiments are set up to verify the resonance point validity for calcium ions and to perform a sensitivity analysis of the biological system response around this point. In the second part, the main attention is paid to the verification of the validity of maximum biological effect at specific BAC/BDC ratios, reflecting the ion parametric resonance theory as well as the current state of the art and knowledge. Both series of experiments have shown interesting results, confirming 29.89 Hz as a bioactive frequency for a BAC/BDC ratio of 1.8. These observations are in close correlation with the assumptions of the ion parametric resonance model for targeting calcium ions. The performed sensitivity analysis around the resonance point looks even more interesting, as conducted preliminary experiments resulted in two more pronounced bioactive frequencies of 24.89 and 34.89 Hz, in terms of biological response significance, than the resonance frequency assumed by an investigated prediction model. The narrow width of the peak, which is typical for physical phenomena of resonant nature, was not observed within this series of experiments, which would be addressed more precisely in future work of authors