A Flexible Transmuted Darna Distribution for Analyzing COVID-19 Pandemic Data

Accurate modeling of COVID-19 mortality rates is essential for understanding the pandemic’s impact and informing public health strategies which directly contributes to SDG 3 (good health and well being). This study investigates the application of the transmuted Darna distribution, a flexible generalized probability distribution to COVID-19 mortality data characterized by skewness and heavy tails. Beyond fitting the mortality rate data, the paper comprehensively explores the key statistical properties of transmuted Darna distribution including, moments, mean, moment generating and characteristic functions, ordered statistics, income distribution curves. Further simulation and parameter estimation using MLE was performed. Additionally, reliability analysis is conducted to assess the behavior of extreme mortality events and distribution’s robustness in risk modeling. Comparative goodness of fit measures demonstrate the superior adaptability of transmuted Darna distribution over other models.