Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions

Amaury De Souza,Ana Paula Garcia Oliveira,José Francisco De Oliveira-Júnior,Débora Aparecida Da Silva Santos,Elania Barros Da Silva

doi:10.33448/rsd-v9i8.6501

Abstract

Objective: To analyze the adjustments of the weibull, gamma, normal and logistic probability density distributions of the historical series of hospitalizations for respiratory diseases (childhood and adult pneumonia) from 2011 to 2015, in Campo Grande, MS. Methods: The shape and scale parameters of the distributions were determined to verify the quality of the data fit. Results: Four probability density functions (Table 2) were fitted and the R2, MAE, RSME, MAPE tests were used to verify the best density function for hospitalization data. Conclusion: The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande.

Highlights

IntroductionSeveral studies of adjustment of probability density distribution or probability estimates using theoretical models of probability in relation to a historical series of data have been developed, highlighting the benefits in the planning of activities that minimize the risks, among which can be cited: precipitation (Catalunha, et al, 2002; Dallacort, et al, 2011; Hartmann, et al, 2011; Rodrigues, et al, 2014; Kist & Virgens Filho, 2015), air temperature (Assis, et al, 2018; Araújo, et al, 2010), concentration of pollutant gases (Souza, et al, 2018a; 2018b), for the historical series of hospital admissions for respiratory diseases there are no published works with this methodology.The use of probability density functions is directly linked to the nature of the data to which they relate
The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande
All hospitalizations occurred in the period between January 1st, 2011 and December 31st, 2015, the diseases investigated were coded according to the International Classification of Diseases (CID) 10th Revision, Pneumology (J17)

Summary

Introduction

Several studies of adjustment of probability density distribution or probability estimates using theoretical models of probability in relation to a historical series of data have been developed, highlighting the benefits in the planning of activities that minimize the risks, among which can be cited: precipitation (Catalunha, et al, 2002; Dallacort, et al, 2011; Hartmann, et al, 2011; Rodrigues, et al, 2014; Kist & Virgens Filho, 2015), air temperature (Assis, et al, 2018; Araújo, et al, 2010), concentration of pollutant gases (Souza, et al, 2018a; 2018b), for the historical series of hospital admissions for respiratory diseases there are no published works with this methodology.The use of probability density functions is directly linked to the nature of the data to which they relate. Several studies of adjustment of probability density distribution or probability estimates using theoretical models of probability in relation to a historical series of data have been developed, highlighting the benefits in the planning of activities that minimize the risks, among which can be cited: precipitation (Catalunha, et al, 2002; Dallacort, et al, 2011; Hartmann, et al, 2011; Rodrigues, et al, 2014; Kist & Virgens Filho, 2015), air temperature (Assis, et al, 2018; Araújo, et al, 2010), concentration of pollutant gases (Souza, et al, 2018a; 2018b), for the historical series of hospital admissions for respiratory diseases there are no published works with this methodology. Several epidemiological studies in recent years have reported associations between high levels of climatic changes and increased rates of death and hospitalization for respiratory and cardiovascular diseases (Mahiyuddin, et al, 2013; Li, et al, 2016). Some epidemiological studies show that air pollution affects human health, even concentrations of air pollutants are below the air quality standards (Souza, et al, 2012)

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Results

Conclusion