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An Intelligent Mechanism for COVID-19 Emergency Resource Coordination and Follow-Up Response
Hongyan Sun1, Wei Xu1, Yinyun Yu2
1School of Management, Shenyang University of Technology, Shenyang 110870, China.
Computational Intelligence and Neuroscience
|June 24, 2022
Summary
This study optimizes emergency supply responses for COVID-19 outbreaks. Findings guide timely material deployment to manage patient surges and reduce supply waste.
Area of Science:
- Operations Research
- Public Health
- Epidemiology
Background:
- The 2020 COVID-19 pandemic highlighted critical challenges in emergency supply chain management.
- Effective response requires understanding both short-term needs and long-term epidemic trends.
Purpose of the Study:
- To analyze and optimize regional resource coordination for emergency supplies during the COVID-19 pandemic.
- To develop a predictive model for long-term epidemic trends and associated supply demands.
Main Methods:
- A multiobjective decision-making approach was used for early-stage resource coordination.
- A system dynamics model was established to simulate epidemic development and predict supply needs.
Main Results:
- Time and satisfaction are key factors in early-stage material deployment decisions.
- Minor illness patient numbers peak around day 20, severe cases around day 40, guiding phased supply allocation.
Conclusions:
- Optimized material deployment strategies are crucial for effective epidemic control.
- Phased supply chain management based on predicted patient peaks enhances efficiency and reduces waste.
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