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Defective Survival Modeling and Cure Rate Analysis of COVID-19: A Cross-Location Comparative Study Using Parametric and Non-Parametric Approaches with Demographic Insights
Modelado de supervivencia defectuosa y análisis de la tasa de curación del COVID-19: un estudio comparativo entre localizaciones usando enfoques paramétricos y no paramétricos con perspectivas demográficas
DOI:
https://doi.org/10.15446/rce.v48n1.115842Keywords:
Cure rate, Cross-location, Defective modeling, Survival analysis (en)Análisis de supervivencia, Comparación entre ubicaciones, Modelado defectuoso, Tasa de curación. (es)
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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.
La pandemia de COVID-19 ha causado una morbilidad y mortalidad sustancial a nivel global desde diciembre de 2019. Este estudio tiene como objetivo modelar las tasas de supervivencia y curación de pacientes con COVID-19 utilizando técnicas avanzadas de modelado defectuoso y métodos sofisticados de aprendizaje automático para mejorar la precisión de las predicciones. Aplicamos una variedad de enfoques estadísticos, incluyendo métodos paramétricos, semi paramétricos y no paramétricos, para ajustar modelos establecidos y novedosos a los datos de supervivencia del COVID- 19, con un enfoque particular en la Distribución de Gompertz Defectuosa. Según nuestro conocimiento, este estudio representa el uso pionero de técnicas de modelado defectuoso para estimar tasas de curación en investigaciones relacionadas con COVID-19. Además, realizamos un análisis comparativo entre diferentes ubicaciones y países utilizando datos geográficos y demográficos de nuestro conjunto de datos. Esta exploración buscó identificar variaciones en las tasas de supervivencia y curación influenciadas por factores como el nivel socioeconómico (NSE), la residencia urbana frente a rural y el acceso a la atención médica. Nuestros hallazgos revelaron disparidades significativas en las tasas de supervivencia y curación asociadas con variables demográficas como la edad, el género, el NSE, la urbanización y el acceso a los servicios de salud. Adicionalmente, el estudio evaluó el impacto de diversas intervenciones de salud pública e identificó mejores prácticas implementadas por diferentes países. En general, nuestros resultados aportan información valiosa a los esfuerzos en curso para comprender y mitigar el impacto del COVID-19 mediante técnicas sólidas de modelado estadístico y aprendizaje automático. Estos hallazgos son cruciales para informar políticas e intervenciones de salud pública a nivel mundial.
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