Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation

Computer Science > Artificial Intelligence COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 16 Mar 2026] Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation Sadaf Tabatabaee, Hicham El Baz, Mohammed Khalil Ghali, Nagendra N. Nagarur 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. Comments: 6 pages. Published in Proceedings of the IISE Annual Conference & Expo 2025. DOI: https://doi.org/10.21872/2025IISE_6202 Subjects: Artificial Intelligence (cs.AI) MSC classes: Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Systems and Control (eess.SY) Cite as: arXiv:2603.15960 [cs.AI]   (or arXiv:2603.15960v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.15960 Focus to learn more Submission history From: Sadaf Tabatabaee [view email] [v1] Mon, 16 Mar 2026 22:22:10 UTC (1,038 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)