Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework.

ABSTRACT: Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021-2023), we quantified LoS across three distinct variant phases (Pre-Delta, Delta, and Omicron) and three age groups (0-39, 40-64, and 65+ years). A gamma-distributed multi-state model-capturing transitions between semi-critical and critical wards-incorporated variant phase and age as log-linear covariates. Parameters were estimated via maximum likelihood with 95% confidence intervals derived from bootstrap resampling, and Monte Carlo iterations yielded detailed LoS distributions. Omicron-phase stays were 5-8 days, shorter than the 10-14 days observed in earlier phases, reflecting improved treatment protocols and reduced virulence. Younger adults typically stayed 3-5 days, whereas older cohorts required 8-12 days, with prolonged admissions (over 30 days) clustering in the oldest group. These time-dependent transition probabilities can be integrated with real-time bed-availability alert systems, highlighting the need for variant-specific ward/ICU resource planning and underscoring the importance of targeted management for elderly patients during current and future pandemics.

SUBMITTER: Choi M 

PROVIDER: S-EPMC12299293 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature