Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911520981516288 |
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| author | Tabatabaee, Sadaf Baz, Hicham El Ghali, Mohammed Khalil Nagarur, Nagendra N. |
| author_facet | Tabatabaee, Sadaf Baz, Hicham El Ghali, Mohammed Khalil Nagarur, Nagendra N. |
| contents | The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing a time series prediction model to forecast patient arrival rates. Using historical data on COVID-19 cases and hospitalizations, the model will generate accurate forecasts of future patient volumes. This will enable hospitals to proactively plan resource allocation and patient flow. The second com- ponent is a simulation model that evaluates the impact of different patient relocation strategies. The simulation will account for factors such as bed availability, staff capabilities, transportation logistics, and patient acuity to optimize the placement of patients across networked hospitals. Multiple scenarios will be tested, including inter-hospital trans- fers, use of temporary care facilities, and adaptations to discharge protocols. By combining predictive analytics and simulation modeling, this research aims to provide hospital administrators with a comprehensive decision-support tool. The proposed framework will empower them to anticipate demand, simulate relocation strategies, and imple- ment optimal policies to distribute patients and resources. Ultimately, this work seeks to enhance the resilience of healthcare systems in the face of COVID-19 and future pandemics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15960 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation Tabatabaee, Sadaf Baz, Hicham El Ghali, Mohammed Khalil Nagarur, Nagendra N. Artificial Intelligence Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Systems and Control (eess.SY) The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing a time series prediction model to forecast patient arrival rates. Using historical data on COVID-19 cases and hospitalizations, the model will generate accurate forecasts of future patient volumes. This will enable hospitals to proactively plan resource allocation and patient flow. The second com- ponent is a simulation model that evaluates the impact of different patient relocation strategies. The simulation will account for factors such as bed availability, staff capabilities, transportation logistics, and patient acuity to optimize the placement of patients across networked hospitals. Multiple scenarios will be tested, including inter-hospital trans- fers, use of temporary care facilities, and adaptations to discharge protocols. By combining predictive analytics and simulation modeling, this research aims to provide hospital administrators with a comprehensive decision-support tool. The proposed framework will empower them to anticipate demand, simulate relocation strategies, and imple- ment optimal policies to distribute patients and resources. Ultimately, this work seeks to enhance the resilience of healthcare systems in the face of COVID-19 and future pandemics. |
| title | Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation |
| topic | Artificial Intelligence Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Systems and Control (eess.SY) |
| url | https://arxiv.org/abs/2603.15960 |