Defective Survival Modeling and Cure Rate Analysis of COVID-19: A Cross-Location Comparative Study Using Parametric and Non-Parametric Approaches with Demographic Insights

Abstract

The COVID-19 pandemic has inflicted substantial global morbidity and mortality since December 2019. This study endeavors to model the survival and cure rates of COVID-19 patients using advanced defective modeling techniques and leveraging sophisticated machine learning methods to enhance prediction accuracy. We applied a range of statistical approaches—including parametric, semi-parametric, and non-parametric methods—to fit established and novel models to COVID-19 survival data, with a particular focus on the Defective Gompertz Distribution. To our knowledge, this study represents the pioneering use of defective modeling techniques for estimating cure rates in COVID-19 research. Furthermore, we conducted a comparative analysis across different locations and countries using geographical and demographic data from our dataset. This exploration aimed to uncover variations in survival and cure rates influenced by factors such as socioeconomic status (SES), urban versus rural residence, and healthcare accessibility. Our findings revealed significant disparities in survival and cure rates associated with demographic variables such as age, gender, SES, urbanicity, and healthcare access. Additionally, the study assessed the impact of various public health interventions and identified best practices implemented by different countries. Overall, our results contribute valuable insights to ongoing efforts aimed at comprehending and mitigating the impact of COVID-19 through robust statistical and machine learning modeling techniques. These findings are crucial for informing public health policies and interventions worldwide.