Modelling Human Mortality Data: A Comparative Analysis of Non-Linear Growth Models Under Error Assumptions
Conference
65th ISI World Statistics Congress 2025
Format: CPS Abstract - WSC 2025
Keywords: additive, covid-19, data;, epidemics, error,
Session: CPS 31 - Experimental Design and Clinical Trials
Wednesday 8 October 4 p.m. - 5 p.m. (Europe/Amsterdam)
Abstract
This study compares four non-linear growth models viz: Mitscherlich, Weibull, Gompertz and Richard under some error assumptions. The human mortality data on a year COVID -19 epidemic of 2020/2021are used. Initial parameters were obtained and compared using information criterion and probabilities under error assumptions to get the appropriate growth models fitting the data. Results showed the parameter estimates, standard errors and p values, thus showing Mitscherlich Additive error model to be the best fitting the data when compared to the other models for an epidemic outbreak.