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Optimal resource allocation model for COVID-19: a systematic review and meta-analysis
Yu-Yuan Wang1,2, Wei-Wen Zhang1,2, Ze-Xi Lu1,2
1Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, 832003, PR China.
BMC Infectious Diseases
|February 14, 2024
Summary
Optimizing resource allocation, particularly for health specialists and vaccines, significantly improves infectious disease control. This systematic review highlights effective strategies for managing limited resources during outbreaks.
Area of Science:
- Epidemiology
- Health resource management
- Mathematical modeling
Background:
- Infectious disease outbreaks strain health resources, necessitating effective resource optimization for robust prevention and control.
- This study systematically evaluates coronavirus disease (COVID-19) resource allocation models and their impact on epidemic control.
Approach:
- A systematic literature search was conducted across multiple databases (2019-2023).
- Two reviewers independently assessed study quality and extracted data.
- Publication bias and sensitivity analyses were performed.
Key Points:
- Propagation dynamics models (59.09%) were most common for resource allocation simulation.
- Differential equation modeling and machine learning algorithms were frequently used for optimization.
- Optimal resource allocation demonstrated significant epidemic control, with an average efficiency of 0.38.
Conclusions:
- Resource allocation models vary based on data availability and simulation duration.
- Health specialists (0.48) and vaccines (0.47) showed the highest control efficiency.
- Optimizing medical staff and vaccine allocation is crucial for effective epidemic prevention.
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