Statistical Analysis of Ebola Virus Disease: A Regression Analysis Approach
The classical regression model cannot capture excess skewness and kurtosis in most of the deadliest diseases data such as Ebola, cancer, cholera, Lassa fever etc. In this paper, we aimed at developing a model, but the descriptive statistics and Exploratory Data Analysis (EDA) was first conducted to know the direction and nature of the data. However, we developed a generalized distribution using the method of transformation, where we re-parameterized the parameters of the distribution called generalized weighted Weibull to capture and accommodate the nature (skewness and kurtosis) of the data used and some of its statistical properties were investigated. The parameter estimate is obtained using maximum likelihood. The performance of the developed distribution was determined using a secondary data on Chronology of previous Ebola virus disease outbreaks in eighteen countries of the world between 1976-2016. The explanatory variables (x_i's) are reported cases and species of the Ebola, while y being the response variable: number of death in each reported case. The method of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Consistent Akaike Information Criterion (CAIC) were used for assessment. The result obtain from the developed generalized weighted Weibull regression model was Death=0.8856 +1.6907(cases)+4.3665 (Types). Therefore, the result revealed that there is an increase in y for a unit change in x_1 and x_2.The proposed model is appropriates and capable of handling this type of data even data that is more skewed.
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