A mixture cure modeling framework for survival analysis of hospitalized COVID-19 patients: implications for hospital resource management.

Publication date: Jul 08, 2026

COVID-19 has caused substantial global morbidity and mortality, placing unprecedented strain on hospital systems. Understanding heterogeneity in survival outcomes and identifying subgroups with distinct mortality risk profiles are essential for improving preparedness in future pandemics. Traditional survival models do not account for the presence of patients who are effectively cured within a clinically meaningful window. Mixture cure models offer a framework for simultaneously estimating the probability of long-term survival and the time-to-event among uncured individuals. This study applies a mixture cure modeling approach to evaluate mortality risk factors among hospitalized COVID-19 patients and to derive statistical insights relevant to hospital resource planning. Data were obtained from 1,998 patients enrolled in the Khorshid COVID Cohort (KCC), a prospective hospital-based study with one-year follow-up. The outcome was COVID-19 death occurring during hospitalization or within 12 months post-discharge. Patients who remained alive throughout the one-year follow-up window were classified as cured. The analysis accounted for exact, right-censored, and interval-censored event times within the mixture cure model. Feature selection was performed using random survival forests and binary random forests, followed by interaction identification with decision trees. The cure component was modeled using logistic regression, while Weibull and Cox proportional hazards models characterized time-to-death among uncured individuals. Older age was a strong predictor of mortality, with hazard ratios of 2. 30 for ages 65-74 and 3. 32 for ages > 74 compared with

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