一种成本敏感的决策模型,用于在对COVID-19等传染病的大规模监测中进行高效的聚合测试
1School of Artificial Intelligence and Big Data, Chongqing Industry Polytechnic College, Chongqing, 401120, China.
Scientific reports
|August 11, 2024
概括
优化用等级分数空间进行聚合测试可以大大降低成本. 新的模型将测试费用减少近一半,提高了大规模传染病检测的效率.
科学领域:
- 公共卫生 公共卫生
- 传染病建模 传染病建模
- 计算生物学 计算生物学
背景情况:
- 随着COVID-19的爆发,人们越来越需要有效的,大规模的传染病监测.
- 传统的聚合测试方法在优化组大小的成本效益方面面临挑战.
- 确定每个组的最佳样本数量对于最大限度地降低整体测试费用至关重要.
研究的目的:
- 引入一种用于优化组分配在聚合测试中的新方法.
- 开发一个成本敏感的多细分智能决策模型,以最大限度地降低聚合测试成本.
- 探索层次化的群体优化策略,以提高效率和降低成本.
主要方法:
- 利用等级分数空间,这是模糊等价关系的延伸,用于组分配优化.
- 提出了一个成本敏感的多细分智能决策模型,包括测试和收集成本.
- 在MATLAB R2022a中进行实验模拟,使用固定个体和正概率.
主要成果:
- 拟议的模型显著提高了聚合测试的效率,并降低了总体成本.
- 最佳的分组策略导致与传统方法相比,测试成本减少了近50%.
- 多颗粒度的方法进一步优化了层次分组,从而节省了大量的成本.
结论:
- 层次分数空间和多颗粒度模型为优化聚合测试提供了有效的解决方案.
- 这些方法可以大幅降低成本,提高效率,这对于未来的流行病防备至关重要.
- 这项研究证明了一种可行的方法,可以提高大规模传染病查的经济可行性.
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