Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort
Abstract Background Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlyi...
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BioMed Central
2025-01-01
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| Series: | Genomics & Informatics |
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| Online Access: | https://doi.org/10.1186/s44342-024-00035-y |
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| author | Frank Aimee Rodrigue Ndagijimana Taesung Park |
| author_facet | Frank Aimee Rodrigue Ndagijimana Taesung Park |
| author_sort | Frank Aimee Rodrigue Ndagijimana |
| collection | DOAJ |
| description | Abstract Background Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients. |
| format | Article |
| id | doaj-art-fff5a98b529e4a6aa7e0b067534de13a |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-fff5a98b529e4a6aa7e0b067534de13a2025-02-02T12:06:29ZengBioMed CentralGenomics & Informatics2234-07422025-01-0123111110.1186/s44342-024-00035-yAnalyzing COVID-19 progression with Markov multistage models: insights from a Korean cohortFrank Aimee Rodrigue Ndagijimana0Taesung Park1Interdisciplinary Program in Bioinformatics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityAbstract Background Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients.https://doi.org/10.1186/s44342-024-00035-yCOVID-19 progressionMarkov multistate modelTransition intensityComorbiditySeverity state |
| spellingShingle | Frank Aimee Rodrigue Ndagijimana Taesung Park Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort Genomics & Informatics COVID-19 progression Markov multistate model Transition intensity Comorbidity Severity state |
| title | Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort |
| title_full | Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort |
| title_fullStr | Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort |
| title_full_unstemmed | Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort |
| title_short | Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort |
| title_sort | analyzing covid 19 progression with markov multistage models insights from a korean cohort |
| topic | COVID-19 progression Markov multistate model Transition intensity Comorbidity Severity state |
| url | https://doi.org/10.1186/s44342-024-00035-y |
| work_keys_str_mv | AT frankaimeerodriguendagijimana analyzingcovid19progressionwithmarkovmultistagemodelsinsightsfromakoreancohort AT taesungpark analyzingcovid19progressionwithmarkovmultistagemodelsinsightsfromakoreancohort |