使用人口优化的COVID-19预测模型中的公平性校正:算法开发和验证研究
Naman Awasthi1, Saad Abrar1, Daniel Smolyak1
1Department of Computer Science, University of Maryland, 8125 Paint Branch Ave, College Park, MD, 20742, United States, 1 2402806921.
Online journal of public health informatics
|February 3, 2026
概括
本研究介绍了人口优化 (DemOpts),这是一种提高COVID-19病例预测公平性的新方法. DemOpts减少了跨种族和种族群体的预测错误,从而使公共卫生资源分配更加公平.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- COVID-19预测模型对于资源配置和干预策略至关重要.
- 最先进的模型利用多式联运数据,但遭受影响少数群体的报告不足和抽样偏见.
- 这些偏见导致不同种族和种族的人口统计学中的COVID-19预测不公平.
研究的目的:
- 引入一种新的公平性校正方法,用于集成级的COVID-19病例预测.
- 提高用于公共卫生决策中的预测模型的公平性.
主要方法:
- 利用硬和软误差平价分析来评估公平性框架.
- 提出并实施了人口优化 (DemOpts),这是一种用于深度学习模型的退化方法.
- 与现有的公平性纠正方法对比测试的DemoOpts.
主要成果:
- 在最先进的COVID-19模型中,在种族和种族群体的平均预测错误中显示出显著的差异.
- 展示了DemOpts与其他退化方法相比,实现了更高的错误平价.
- 证实DemOpts有效地减少了人口群体之间的平均错误分布的差异.
结论:
- 人口优化 (DemOpts) 作为一种有效的方法来减少错误平价差异.
- 与现有的文献方法相比,DemOpts产生了更公平的COVID-19预测模型.
- 该方法提高了公平公共卫生规划预测的可靠性.
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