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Climate change has a high impact on health and morbidity/ mortality in respiratory system diseases and remains poorly investigated in probability distribution modeling. The objective of this study was to analyze the adjustments of Burr (Bu), Inv Gausian 3P (IG3P), Lognormal (LN), Pert (Pe), Rayleigh 2P (Ra 2P) and Weibull 3P (W3P) distributions of the historical series of hospitalizations for respiratory diseases (total hospital admissions) for the period from 2004 to 2018, in Campo Grande, MS. For the data series, the shape and scale parameters of the distributions were determined to verify the quality of fit of the observation data, the Goodness-of-Fit Tests (GOF): Kolmogorov-Smirnov Test, Anderson -Darling Test, Chi-square Test tests were used to verify an optimal estimate for the hospitalization data hospital. All PDFs are able to describe well the characteristics of hospitalizations. The results presented (total admissions), (summer) show that the functions Weibull 3P (W3P) and Inv Gausian 3P (IG3P); (fall) show that the functions Burr and Weibull 3P (W3P); (winter) shows that the Burr and Inv Gausian 3P (IG3P) and (spring) functions for lognormal and Rayleigh (2P) functions provided the best observed fit for hospital admissions.

Statistical Behavior of Hospital Admissions for Respiratory Diseases by Probability Distribution Functions