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COVID-19 outbreak: A data-driven optimization model for allocation of patients
Sobhan Sarkar1, Anima Pramanik2, J Maiti2,3
1Division of Management Science, Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh-EH8 9JS, UK.
Computers & Industrial Engineering
|September 15, 2021
Summary
This study presents an optimization model for efficient COVID-19 patient allocation during healthcare resource constraints. The model identifies key factors for effective patient distribution to manage the pandemic response.
Area of Science:
- Public Health
- Operations Research
- Epidemiology
Background:
- The COVID-19 pandemic has overwhelmed global healthcare systems, leading to resource shortages and delayed patient admissions.
- This crisis necessitates innovative solutions for managing patient flow and optimizing hospital capacity.
Purpose of the Study:
- To develop a data-driven optimization model for effective patient allocation in hospitals amidst the COVID-19 pandemic.
- To identify the most affected cities and critical factors influencing patient distribution.
Main Methods:
- A compartmental model was developed to characterize COVID-19 spread.
- Pareto analysis was used to identify highly affected cities.
- An optimization model was created for patient allocation, validated with Indian city data.
- Sensitivity analysis was performed to assess model robustness.
Main Results:
- The study identified ten cities as most affected by COVID-19.
- The optimization model demonstrated efficient strategies for patient allocation.
- Key determinants for patient allocation include cooperation, inter-city distances, patient numbers, and hospital bed capacity.
Conclusions:
- The proposed data-driven model offers effective strategies for optimizing patient allocation in resource-constrained healthcare settings.
- Understanding key influencing factors is crucial for managing pandemic-related healthcare challenges.
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