Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics
During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidem...
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| Veröffentlicht in: | The Journal of the Operational Research Society Jg. ahead-of-print; H. ahead-of-print; S. 1 - 15 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Taylor & Francis
02.04.2024
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| Schlagworte: | |
| ISSN: | 0160-5682, 1476-9360 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics. |
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| ISSN: | 0160-5682 1476-9360 |
| DOI: | 10.1080/01605682.2023.2199769 |