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1736. Fair and Effective Vaccine Allocation During a Pandemic
Invited abstract in session WC-15: COVID-19 (2), stream OR in Health Services (ORAHS).
Wednesday, 12:30-14:00Room: 18 (building: 116)
Authors (first author is the speaker)
| 1. | Eda Yücel
|
| Industrial Engineering, TOBB University of Economics and Technology | |
| 2. | Gunes Erdogan
|
| School of Management, University of Bath | |
| 3. | Sibel Salman
|
| Industrial Engineering, Koc University | |
| 4. | Parinaz Kiavash
|
| Koç University |
Abstract
This study introduces a novel model addressing the Vaccine Allocation Problem (VAP), focusing on distributing vaccines across different population locations over multiple pandemic periods. It incorporates disease progression and vaccination effects to minimize total expected mortality and location-based mortality inequalities while considering constraints like vaccine supply and healthcare capacities. Utilizing an extended Susceptible-Infected-Recovered (SIR) epidemiological model, the VAP is formulated as a nonlinear mixed-integer programming problem and solved using the Gurobi solver. Through a series of scenarios spanning 12 weeks in the UK, the study highlights the significant impact of vaccine availability and disease spread parameters on optimizing vaccination strategies.
Keywords
- Programming, Nonlinear
- Disaster and Crisis Management
- Health Care
Status: accepted
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