Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework
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),...
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MDPI AG
2025-07-01
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| author | Minchan Choi Jungeun Kim Heesung Kim Ruarai J. Tobin Sunmi Lee |
| author_facet | Minchan Choi Jungeun Kim Heesung Kim Ruarai J. Tobin Sunmi Lee |
| author_sort | Minchan Choi |
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| description | 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. |
| format | Article |
| id | doaj-art-9c10a80f90c54d3da09c294eecc25beb |
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| issn | 1999-4915 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Viruses |
| spelling | doaj-art-9c10a80f90c54d3da09c294eecc25beb2025-07-25T13:38:56ZengMDPI AGViruses1999-49152025-07-0117795310.3390/v17070953Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting FrameworkMinchan Choi0Jungeun Kim1Heesung Kim2Ruarai J. Tobin3Sunmi Lee4Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Republic of KoreaDepartment of Mathematics and Computer Science, Korea Science Academy of KAIST, Busan 47162, Republic of KoreaDepartment of Internal Medicine, College of Medicine, Chungbuk National University Hospital, Chungbuk National University, Cheongju 28644, Republic of KoreaMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, AustraliaDepartment of Applied Mathematics, Kyung Hee University, Yongin 17104, Republic of KoreaUnderstanding 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.https://www.mdpi.com/1999-4915/17/7/953COVID-19hospital length of staySouth Koreamulti-state modelparameter estimation |
| spellingShingle | Minchan Choi Jungeun Kim Heesung Kim Ruarai J. Tobin Sunmi Lee Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework Viruses COVID-19 hospital length of stay South Korea multi-state model parameter estimation |
| title | Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework |
| title_full | Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework |
| title_fullStr | Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework |
| title_full_unstemmed | Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework |
| title_short | Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework |
| title_sort | multiscale modeling of hospital length of stay for successive sars cov 2 variants a multi state forecasting framework |
| topic | COVID-19 hospital length of stay South Korea multi-state model parameter estimation |
| url | https://www.mdpi.com/1999-4915/17/7/953 |
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